A survey of bio-inspired robotics hands implementation: New directions in dexterous manipulation

Robotics and Autonomous Systems ( ) –

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Robotics and Autonomous Systems

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A survey of bio-inspired robotics hands implementation: New directions indexterous manipulationEbrahim Mattar ∗,1

Department of Electrical and Electronics Engineering, College of Engineering, University of Bahrain, P.O. Box. 13184, Bahrain

a r t i c l e i n f o

Article history:Received 21 September 2010Received in revised form9 December 2012Accepted 21 December 2012Available online xxxx

Keywords:BiomimeticsRobotics handsDexterous manipulationArtificial musclesHand actuation

a b s t r a c t

Recently, significant advances have been made in ROBOTICS, ARTIFICIAL INTELLIGENCE and otherCOGNITIVE related fields, allowing tomakemuch sophisticated biomimetic robotics systems. In addition,enormous number of robots have been designed and assembled, explicitly realize biological orientedbehaviors. Towards much skill behaviors and adequate grasping abilities (i.e. ARTICULATION andDEXTEROUS MANIPULATION), a new phase of dexterous hands have been developed recently withbiomimetically oriented and bio-inspired functionalities. In this respect, this manuscript brings a detailedsurvey of biomimetic based dexterous robotics multi-fingered hands. The aim of this survey, is to findout the state of the art on dexterous robotics end-effectors, known in literature as (ROBOTIC HANDS) or(DEXTEROUSMULTI-FINGERED) robot hands. Hence, this review finds such biomimetic approaches usinga framework that permits for a common description of biological and technical based hand manipulationbehavior. In particular, the manuscript focuses on a number of developments that have been taking placeover the past two decades, and some recent developments related to this biomimetic field of research. Inconclusions, the study found that, there are rich research efforts in terms of KINEMATICS, DYNAMICS,MODELING and CONTROL methodologies. The survey is also indicating that, the topic of biomimeticinspired robotics systems make significant contributions to robotics hand design, in four main directionsfor future research. First, they provide a genuine world test of models of biologically inspired handdesigns and dexterous manipulation behaviors. Second, they provide novel manipulation articulationsand mechanisms available for industrial and domestic uses, most notably in the field of human like handdesign and real world applications. Third, this survey has also indicated that, there are quite large numberof attempts to acquire biologically inspired hands. These attempts were almost successful, where theyexposed more novel ideas for further developments. Such inspirations were directed towards a numberof topics related (HAND MECHANICS AND DESIGN), (HAND TACTILE SENSING), (HAND FORCE SENSING),(HAND SOFT ACTUATION) and (HANDCONFIGURATIONAND TOPOLOGY). FOURTH, in terms of employingAI related sciences and cognitive thinking, it was also found that, rare and exceptional research attemptswere directed towards the employment of biologically inspired thinking, i.e. (AI, BRAIN AND COGNITIVESCIENCES) for hand upper control and towards much sophisticated dexterous movements. Throughoutthe study, it has been found there are number of efforts in terms of mechanics and hand designs, tacticalsensing, however, for hand soft actuation, it seems this area of research is still far away from having arealistic muscular type fingers and hand movements.

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1. Introduction

1.1. Bio-inspired dexterous robotics hands manipulation

As defined, robotics BIOMIMETIC is the study of the structureand function of biological systems, as models for the designand engineering of materials, machines, and mechanics forrobotics applications. The accelerating swiftness of advancements

∗ Tel.: +973 39632302.E-mail addresses: [email protected], [email protected].

1 MIEEE, MIET. Author has earlier published previous articles under the name of(Ebrahim. Al-Gallaf).

in the field of biomimetics seems to make evident that theemergence of machines as our peers is imminent. Althoughthis theme brings with it huge implications and new directionsincluding, and not limited, to nature of progression and its rolein technological progression. The technology is greatly benefitedfrom different fields of knowledge as Psychology of BiomimeticRobots, Integrative Biology, Biomimetic Animated Creatures,Artificial Life, Functionality Elements of Biomimetic Robots, andApplications for Biologically Inspired Intelligent Robotics.

In recent years, major progresses have been made in Control,Robotics, Artificial Intelligence and others disciplines allowing tomake sophisticated biomimetic systems. Working together, aca-demicians, scientists and engineers are now reverse engineering

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Fig. 1. Initial attempts to emulate humangrasps. Fingersmovements are entirely controlled via tendondriven joints,with solid fingers structures. (i): Belgrade/USCHand, [1].(ii): 1994 Reading University - Cybernetics Hand, CYBHAND, [5]. (iii): Utah Hand, [6]. (iv): JPL Hand, [7].

many of both animal’s and human’s performance characteristicsusing these advances. An interdisciplinary research has resultedin machines that can identify facial expressions, comprehendspeeches, and locomotion in robust bipedal gaits, similar to acertain extent to humans movements and actions. Related to thiscontext, advances in polymer sciences have also resulted in actua-tions based on Electro-active Polymers (EAP), and are also knownlater as ARTIFICIAL MUSCLES. Artificial muscles are such materi-als that show functional characteristics remarkably similar to bio-logical muscles. Human like hands are important for robotics, andthey are such of the most complex organs of the human body af-ter brain; thus, it is clearly understood why its behavior had in-tensively interested former philosophers. Over the past decades,Human like hands have been object of study and research not onlyin the medical field, but also in the engineering field, Jimmy W.,Soto M., and Gini G., [1]. Robot Efforts have been directed towardsmulti-fingered robot hands because:

(i) Parallel jaw grippers can assume a limited range of configura-tions, since having only one degree of freedom in a hand limitsthe choice of grasping configurations, Salisbury K., [2].

(ii) It is unreasonable to expect large, massive links in a robot armto be able to cover a largeworking volume and simultaneouslyhave high bandwidth and sensitivity, Salisbury K., [2].

(iii) Multi-fingered hands provide for a unique sensing modality,the cooperation of position, force, tactile, and proximitysensors provides an opportunity for obtaining informationabout the mechanical and physical attributes of objects andtasks.

(iv) There is need for mechanical interfacing between the end-effector and the arm due to: (a) Robot oscillation. (b) Robotprogramming errors. (c) Unexpected environmental forcesand displacements. (d) Part fixing errors, Salisbury K., andCraig J., [3].

(v) Dexterous hands can be used in direct human control, wherethey are utilized in hazardous locations.

(vi) The versatility of robot hands arises from the fact that finemotion manipulation can be accomplished through relativelyfast and small motions of fingers, as they can be used fordifferent objects, Zexiang L. et al., [4].

(vii) Contemporary industrial robots are not suited to operating inunstructured environments, and so the range and complexityof tasks they can perform is limited. Multi-fingered handshave the potential for improving on this deficiency, and theircontinued development holds the prospect of the applicationof robot technology to a wider variety of manufacturing tasks,Al Gallaf., et al. [5].

Building biologically inspired intelligent robotics hands requiresclear understanding of the biological models, as well as devel-opments in analytical modeling, graphic simulation, in addition,

physical implementations of related technology. Research and en-gineering focus involved with the development of biologically-inspired intelligent robots are multidisciplinary. This involves thestudy of fingers materials, joints actuating, tactile sensing, handkinematics structures, functionality, movements control, artificialintelligence, and hand autonomy. While the engineering chal-lenges are very interesting to address, however, there are alsofundamental concerns that need attention. Some of such issuesinclude self-defense, controlled-termination as well as many oth-ers. There are already extensive heritages of making {dexterousrobots hands} over the past, that appear and function a similarto biological hand. In its original sense, the term hand manipu-lation applies to the process of directing a grasped object to itsdestination. Over the past two decades, large number of dexter-ous robotics hands have been developed that explicitly emulatehuman like hand shape and movements. However, it was clearthat biological functionalities could not be fully emulated due toluck of the right technologies. In addition, over past few years,engineering such biomimetic intelligent creatures, such as robots,was hindered by physical and technological constraints and limi-tations. The use of artificial intelligence, effective artificial musclesand other biomimetic based technologies are expected to makethe possibility of realistically looking and behaving robotics handsintomore practical. Dexterousmulti-fingered robot hands have be-come of great attention in robotics due to their advantages overconventional grippers in tasks requiring dexterous manipulation.Real-time grasping force optimization is also a difficult problembecause the friction forces between the fingertips and a graspedobject are nonlinear constrains. Literatures in this vital field havebeen focused since the early of 1990’s, at that time, when Bel-grade/USC, Utah, JPL, and Reading University-CybHand hand re-searchers published their research work in progress over earlieryears, refer to Fig. 1, [1,5–7].

In reality, an early survey was also conducted on 1993, as docu-mented inAlGallaf, et al. [5].Within that particularmanuscript, au-thors have presented a study that looked into some early attemptsfor robot hand designs. Due to lack of appropriate technologies onthat early days, wholly bio-inspired hand designs were not easytasks. Over the past few number of years, technology has devel-oped sufficiently, that has allowed more closer hand designs to bi-ological mechanisms, is much reachable. Belgrade/USC Hand [1],Reading CybHand [5], Utah Hand [6], and JPL Hand [7], are justconsidered as early attempts to emulate human grasps. Fingertipsmovements are entirely controlled through tendon driven joints,with solid fingers structures. Biomimetic robot hands, as indicatedby Yoseph C. and Breazeal C. [8], have been also introduced latelyover the last decade. Such field of biologically inspired technology,having the moniker biomimetics, has evolved from making staticcopies of human and animals in the form of statues to the emer-gence of robots that operate with realistic behavior, [8]. This is dueto a number of potential advantages of purely mechanical robothands, as known here as the classical hand.

E. Mattar / Robotics and Autonomous Systems ( ) – 3

1.2. Article contribution

Twenty years ago, i.e. in 1993, the author has put a survey aboutarticulated robotics hands, as in [5]. Over that time, technologywas such emerging to build human like robot hands. With theadvances in technology and computational powers, things arechanging now. Within this manuscript, we are also surveyinga number of attempts towards bio-inspired robot hand designs.There were a number of hand designs and dexterous manipulationearly attempts. This is depicted and shown in Fig. 1. Hand modelsfor such robots are greatly inspired by science fiction (books,movies, toys, animatronics, etc.). These science fiction attemptshave originated perceptions and expectations that are far beyondthe achievement of current engineering capabilities, which areconstrained by physics laws and technology current state-of-the-art. Therefore, in this survey, we provide a background fordescribing hand manipulation phenomena which builds on up-to-date discussions in the biological literature. Furthermore, withinthis framework, we also provide an overview of efforts that havebeen undertaken so far in this field of biomimetic robot handmanipulation. Here we consider an approach as biomimetic ifa researcher try to implement a robotics hand or mechanismdescribed in the biological literature, and explicitly refer to thebiological inspiration of their approach. In addition, within thismanuscript, we shall survey a number of potential researchframeworks that have been focused lately towards biologically-inspired technologies for robot hands design and implementation.In reality, it is not an obvious task to survey all kinds of technologiesrelated to the biomimetic based robot hands designs. However,we shall be dividing the manuscript to a number of robot handsdemanding issues, and take account of a number of inter-relatedtechnologies. This includes, HANDS DESIGN, TACTILE SENSING,ACTUATION systems, and so forth of inter-related robot handtechnologies.

1.3. Article organization

The survey looks in details at recent efforts towards dexterousrobotics hands, and behavior of biological of human hands. Wesurvey in details issues related to biomimetic systems and roboticshands experiences. The manuscript has six main Sections. InSection 1, we present a focused introduction into the manuscripttheme and objectives. Section 2 presents studies related to(Bio-Inspired Dexterous Robotics Hands Topology And Designs),whereas Section 3, it summaries efforts towards (Hand bio-Inspired soft actuation materials). Tactile sensing is an importanttopic for hand designs. Hence, in Section 4 we overview (Bio-Inspired Tactile Sensing). Bionic is also an essential topic forhand designs, hence we present in Section 5 specific experiencesrelated to (Bionic and Prosthetic Hands). Finally, in Section 6, wesummarize few conclusions, and place forward few remarks. Inaddition, we conclude the review with a discussion of technicaland biological relevance of biomimetic approaches to robot handmanipulation.

2. Bio-inspired dexterous robotics hands topology and designs

2.1. Hand biomimetic compliance control structures of human finger

In their challenge, Byoung K. et al. [9], stated that, for anobject grasped by a robotics hand to work in compliance controldomain, they needed to analyze the necessary condition for suc-cessful stiffnessmodulation in anoperational space. Theyproposeda new compliance control method for robot hands which con-sist of two steps. RIFDS (Resolved Inter-Finger Decoupling Solver),through which to decompose a desired compliance characteristicspecified in the operational space into the compliance characteris-tic in the fingertip space, without inter-finger coupling. RIJDS (Re-solved Inter-Joint Decoupling Solver), and this to decompose the

compliance characteristic in the (fingertip space) into the compli-ance characteristic in the (joint space)without inter-joint coupling.It was found, a finger structure should be biomimetic in the sensethat either kinematic redundancy or force redundancy are requiredto implement the proposed compliance control scheme. This canbe clearly seen in Fig. 2. Byoung K. et al. [9] concluded that, thenumber of hand fingers and the structure of each finger are im-portant components for fulfillment of compliance control scheme.In addition, the geometric configuration of the given grasp shouldbe carefully considered to stiffen the stiffness characteristic spec-ified in the operational space. It was also concluded that, for theintroduced bio-inspired two steps control compliance algorithm(RIFDS and RIJDS, Fig. 2-(ii)), the number of fingers and the struc-ture of each finger are important for fulfillment of compliance con-trol scheme. In addition, the geometric configuration of a givengrasp should be carefully considered to satisfy a stiffness charac-teristics specified over an operational space.

2.2. Development of biomimetic robot hand using parallel mecha-nisms

In [10], Lee S. et al., did describe a development of biomimeticrobot hands and its control scheme. Each robot hand has fourunder-actuated fingers, which are driven by two linear actuatorscoupled, refer to Fig. 3. Each fingertip can reach towards objectsby curved surface workspace in 3D-space. The robot hand wasdesigned considering the dexterity and the size suited for humantools and has tactile sensors equipped on the fingertips of eachfinger. The robot hand has four fingers with totally nine DOFsincluding two linear actuators and linkage knuckles. Computersimulations were used to show the performance of the robothand to manipulate tools of various shapes. Lee S. et al. statedthat, according to the study of the human hand, a noticeablepoint is that the motion of coupled muscles generates thefinger movements. Like human hands, the designed robot handhas parallel mechanisms which are actuated by coupled-linearactuators. The mechanisms have high-efficiency, low inertia, largepayload capacity and simple structures. In the compact linearactuator module, a motor, its driver, a microcontroller withcommunication chip and encoder are equipped. Lee S. et al., [10],have also presented the mechanism for an anthropomorphic robothand which was designed by mimicking human hands. Theirresearch also was based on a noticeable point is that the motionof coupled muscles generates the finger movements. Like humanhands, the designed robot hand has parallel mechanisms whichare actuated by coupled-linear actuators. The mechanisms havehigh-efficiency, low inertia, large payload capacity and simplestructures. In the compact linear actuator module, a motor, itsdriver, a microcontroller with communication chip and encoderare equipped. Each finger is composed of two linear actuatormodules and linkage knuckles. In contrast with other fingers, theyhave added one more degree of freedom on the palm of thumb. Inaddition, multi-point tactile sensors are equipped on the fingertipsof each finger. The hand can grasp various objects unknown andknown that can be seen fromour environment. Results have shownthat, it is possible to have good mix between the good mechanicsand good instrumentations tools and probes. Also results haveshown that, the hand can grasp various objects unknown andknown that can be seen from the environment.

2.3. Tendon and muscle force requirements for humanlike forcecapabilities in a robotic finger

Tendon and muscle force requirements for humanlike hand isan important issue. Pollard N. and Richards G., [11], have foundthat adapting human examples to a robotmanipulator is a complex


Fig. 2. Biomimetic compliance control, [9]. A closed-loop structured finger with redundant actuators, number of fingers, and the structure of each finger are important forfulfillment of compliance control scheme. (i)-(Left): A finger with five-bar mechanism. (i)-(Right): Compliance control by three-fingers. (ii): Three fingers block diagram forcompliance control.

Fig. 3. Biomimetic robot hand design. Parallel mechanisms approach, [10]. (Left): Muscles of human hand and features of parallel actuated robot hand. (Right): Kinematicsof coupled linear actuated finger.

issue. This is due to differences between human and robot hands.Force transmission mechanisms in robot fingers are generallysymmetric about flexion/extension axes, but in human fingersthey are focused towards flexion. Refer to Fig. 4. Their researchdescribes how a tendon driven robot finger can be optimized forforce transmission capability equivalent to human index finger. Itwas shown that two distinct tendon arrangements that are similarto those that have been used in robot hands can achieve similar

range of forces as human finger with minimal additional cost intotal muscle requirements.

This researchwas focused to examine: Can a two tendon-drivenfinger design, as in Fig. 4-(left), be tuned to have force capabilitiesequivalent to that of the human finger, as Fig. 4-(middle)? If so,do they represent higher or lower ‘‘cost’’ designs than that of thehuman finger? Cost is formalized as the sum over all tendonsof the maximum force that must be supplied to that tendon.


Fig. 4. Muscle force requirements for humanlike force capabilities, [11]. (Left): (2n) and (n+1) tendon fingers. (Middle): Index Flexor and extensor tendons. (Right): Humanand optimized (2n) robot finger Force capabilities.

Fig. 5. The Zurich–Tokyo hand, as inspired by themuscle–tendon system of the human hand, [12]. (Left): Hand structure. (Middle and Right): Final grasp of different objects.The control is the same, but the behavior is very different.

In Fig. 4-(right), we show a table showing muscle forces for theinitial and optimized designs of each robot finger. The initialdesigns are far fromduplicating the force capabilities of the humanfinger model, while the optimized designs can duplicate theseforce capabilities. It was found that, human force capability canbe matched in a robotics hand design. Matching this and otherproperties such as stiffnessmaymake it easier for us to successfullyimitate human grasps.

Pollard N. and Richards G., [11], have both concluded that,muscle forces and moment arms of a dual tendon driven robotfinger design was optimized, in such a way to duplicate the forcecapabilities of the human finger with very similar total muscleforce requirements. The goodmatch of both force capability (Fig. 4,here only (x–z) plane is shown), and cost sum of muscle forcesis somewhat surprising given that the robot finger designs differfrom the human finger and from each other in number of tendons,roles of each tendon, and tendon moment arms, with the robotfingers designed for simpler manufacture. It is remarkable tonote that, the research was able to employ a linear programmingproblem optimization approach to select the best (n + 1) tendondesign as shown in Fig. 4-(left). There are eight plausible tendonarrangements for the (n + 1) design: the long tendons caneither flex or extend the PIP joint, the long and medium tendonscan either flex or extend the MCP joint, and the long, medium,and short tendons can either abduct or adduct the MCP joint.Pollard N. and Richards G. [11], also concluded that, differentarrangements of tendons or additional tendons wouldmake eithersystem redundant. A number of alternative designs were thereforeevaluated using the technique for redundant combinations oftendons a linear programming problem.

2.4. Zurich–Tokyo hand: inspired by the muscle–tendon system of thehuman hand

In collaboration with the Developmental Cognitive MachinesLaboratory at the University of Tokyo, Pfeifer R. et al., [12],have developed a prosthetic robotic hand inspired by themuscle–tendon system of the human hand. This is known in

literature as the ZURICH–TOKYO hand, or as the YOKOI Hand. Thehand design with its actuation are shown in Fig. 5. In this sense,the robotic hand has a total of (13) degrees of freedom, whereeach finger has been equipped with different types of sensors(i.e., flex/bend, angle, and pressure). At the Biomechatronics lab,medical system engineering department, at Chiba University inJapan, the same robotic hand has been used as a prostheticdevice. For that purpose, few number of EMG signals can beused to interface the deigned robot hand non-invasively to apatient. Electrical stimulation can be used as a substitute fortactile feedback. This tendon driven robot Zurich–Tokyo hand ispartly built from elastic, flexible and deformable materials. Forexample, the tendons are elastic, the fingertips are deformable andbetween the fingers there is also deformable material. The handwas designed to have up to (13) degrees of freedom movement.Such redundant DOF was achieved and are joints movementsdriven by (13) servomotors. Also the hand has a number ofbending sensors, and they are placed on each finger. This willgive an easy means and as a measure of the hand fingers spacepositions. The is also equipped with a set of standard FSR pressuresensors. These pressure sensors do cover the hand different contactlocations. This includes the areas of contacts on fingertips, onthe back of each finger, in addition to area on the hand palm.As indicated before, the Zurich–Tokyo robotic hand has a total(13) degrees of freedommovements, this give the handmechanicsmore space of movements. In addition to this, each finger has beenequipped with different types of tactile and non-tactile contactsensors and devices. This would include (i.e., flex/bend, angle, andpressure). For testing and investigation purposes, more researchwas conducted at the Artificial Intelligence Laboratory at theUniversity of Tokyo. Hence, such a designed robotic hand wasused to investigate the relationship betweenmorphology, intrinsicbody dynamics, generation of information, structure throughsensorimotor coordinated activity, and LEARNING. They havealso implemented biologically inspired learning mechanism toallow the robotic hand to explore its own movement capabilities.Moreover, by correlating the sensory input (from the hand), as aresult of its motor outputs, the Zurich–Tokyo robotics dexterous


Fig. 6. SQUSE lifelike robot hand, designed and implemented by squse company, [13]. Hand skeleton was covered by a skin which is made of soft silicone. The hand ispowered by a series of (22) actuators, and has multiple joints and movable polycarbonate bones.

hand can also learn to manipulate, move, and grasp objects byitself. In [12], it was reported that, because of the morphology ofthe hand, the elastic tendons, and the deformable finger tips, thehand will automatically self-adapt to the object it is grasping. Thisassumes, there is no need for the agent to ‘‘know’’ beforehandwhatthe shape of the to-be-grasped objectwill be. The shape adaptationis taken over by the morphology of the hand, the elasticity ofthe tendons, and the deformability of the finger tips, as the handinteracts with the shape of the object. In this setup, control ofgrasping is very simple, or in other words, very little ‘‘brain power’’is required for grasping.

2.5. Squse lifelike robot hand; polycarbonate skeleton covered by askin which is made of soft silicone

A Japanese based company, known as (Squse Company), asin [13], has built an harshly life-like robotic hand. It was known asthe Robot Hand H-Type. Details are given and depicted in Fig. 6.The hand is powered by a series of actuators and has multiplejoints and movable polycarbonate bones. The robotic hand, iscovered with silicon rubber. Such type of hand cover allows thehand to give a skin-like look. The hand has the ability to liftand move small objects. In reference to [13], the robot handmanufacturing company, says that the hand will mostly find itsuse in industrial applications. It has been also reported that, eventhough it resembles a lifelike hand in a great degree, its mainfunction is not for prosthetics. The Kyoto-based factory automationcompany Squse, has developed the robotic hand, in such away itis dexterous and delicate enough to handle small delicate objects.The hand was built from a polycarbonate skeleton. Hand skeletonwas covered by a skinwhich ismade of soft silicone. Hand skeletonportion itself weights (220 g), and the silicone skinweights (120 g).

The hand design was achieved in such away that, the hand canlift up to (1.5 kg). It is available in two variations. One is morehuman resembling, as this is known as the (H-Type Squse Hand).The other, as known as the (G-Type Squse Hand), is mounted on abase and resembles industrial robots. Both hands are powered by(22) actuators, as they represent pneumatically powered artificialmuscles. They enable its fingers to move as the fingers of a human

hand do. The entire arm is of 16 degrees of freedom. This permitsthe hand to move in a human-like manner. The hand dexterityallows it to have many ways to manipulate objects and differentgrips. This is ranging from a full-hand squeeze to a delicate two-finger pinch used to move delicate and soft objects. The hand canalso handle delicate grips. This is because it is designed to pack,handle and even harvest fruits and vegetables without bruisingthem or to handle fragile goods or factory parts being processed.The entire hand weight is of a total of (340 g). This also allows thehand to be used as a prosthetic limb.

3. Bio-inspired non rotary actuation materials

3.1. Design and control of a shape memory alloy based dexteroushands

In [14], Price A. et al., stated that modern externally poweredupper-body prostheses are conventionally actuated by electricservomotors. It is true that such mechanical motors achievereasonable kinematic performance, however, they are voluminousand heavy. Therefore, there are efforts to avoid using mechanicalactuation. Deterring factors such as these lead to a substantialproportion of upper extremity amputees avoiding the use oftheir prostheses. It was found, it is apparent that there existsa need for functional prosthetic devices that are compact andlightweight. The realization of such a device requires an alternativeactuation technology. Hence, biological inspiration suggests thattendon based systems are advantageous. In particular, shapememory alloys are a type of smart material that exhibit anactuation mechanism resembling the biological equivalent. Assuch, shape memory alloy enabled devices promise to be of majorimportance in the future of dexterous robotics, and of prostheticsin particular. For the Shape Memory Alloys (SMA) details, thepotential application of several alternative lightweight actuators,such as artificial muscles, has been investigated including Electro-Active Polymers and pneumatic muscles with little success. PriceA. et al. research framework did investigate issues surrounding thepractical application of shape memory alloys as artificial muscles


Fig. 7. A shape memory alloy based robotics hand design, [14]. (Left): SMA actuated artificial hand. (Middle): Kinematic structure of the SMA artificial finger. (Right): Singlefinger control algorithm.

in a three-fingered robot hand intended for prosthetic applications,as indicated to in Fig. 7. This resulted in actuators that producehigh output force, but are limited to low strain, while the latterconfiguration results in actuators that produce low force andachieve higher strains. Actuator strains of approximately (32%)have been demonstrated through the exploitation of SMA filamentgeometry which amplifies small localized SMA strains into large-scale actuator contraction. However, these high-strain actuatorsare prone to excessive power requirements and exhibit extremelylimited fatigue life, and are therefore impractical for portableprosthetic applications, [14].

In their research outcomes, Price A. et al. [14], have stated thatthe experience gained through the study has led to the followingnumber of conclusions. First: Shape Memory Alloys have beenshown to provide a feasible ‘‘alternative actuation’’ technologyfor lightweight robot hand applications by means of the shapememory effect. Second: The new nine-DOF prosthetic hand thathas been designed and manufactured, has a mass comparableto a typical commercially available single-DOF prosthetic hand(this can be useful in compensating for additional hand batteryrequirements). Finally: A new sigmoid based control algorithmhas been proposed and evaluated for the position control ofShape Memory Alloy elements. In reality, such a position control,minimizes overshoot to avoid the slow time response inherent tothe passive cooling necessary for SMA elongation.

3.2. A shape memory alloy-based tendon-driven actuation

According to Bundhoo V. et al. [15], a new biomimetic tendon-driven actuation system for prosthetic and wearable robotic handapplications is presented. It is based on the combination of compli-ant tendon cables and one-way shape memory alloy (SMA) wiresthat form a set of agonist–antagonist artificial muscle pairs forthe required flexion/extension or abduction/adduction of the fin-ger joints, as shown in Fig. 8. The performance of the proposedactuation system is demonstrated using a 4 degree-of-freedom(three active and one passive) artificial finger test-bed, also devel-oped based on a biomimetic design approach. A microcontroller-based pulse-width-modulated proportional derivation (PWM-PD)feedback controller and a minimum jerk trajectory feed-forwardcontroller are implemented and tested in an ad hoc fashion to eval-uate the performance of the finger system in emulating naturaljoint motions. The aim of such a research framework was to emu-late the biological features of the natural muscle–tendon arrange-ment in thehumanhand indeveloping anewactuationmechanismfor a biomimetic artificial finger, Fig. 8. In that sense, the main fea-tures of the proposed biomimetic actuation and finger system canbe summarized as: (i) anthropomorphically accurate size and ap-pearance; (ii) kinematically accurate joint motion, (iii) compliantand tendon-drivenmuscle-like actuation, and (iv) biomimetic sen-sory feedback.

3.3. Actuation via artificial muscles via electro-active polymers

In 2000, Cohen Y. [16], has reported that, one of the importantaspects of making biologically inspired robots, is the developmentof actuators that allow emulating the behavior and performanceof real human-like muscles. Capability for human-like musclesactuators is increasingly becoming feasible with the emergenceof the Electro-Active Polymers (EAP). EAP are also known asartificial muscles [16]. EAP materials have functional similaritiesto biological muscles. In this regards, Cohen Y. [16], has alsogiven a detailed study about such materials. This is shown clearlyin Fig. 9-(i), where over the left figure, it is illustrated theEAP infrastructure and areas needing attention, Over the rightdiagram, it is shown a schematic diagram of the basic componentsof an EAP-driven system, [16]. This includes flexibility, damagetolerance, and large actuation strains, stretching, contractingor even bending. The author’s view of this infrastructure, andareas needing simultaneous development are shown schematicallyhere. This involves the need for adequate understanding of EAPmaterials’ behavior and the necessity to assure their durability inservice. Enhancement of the actuation force requires knowledgeof the basic principles using computational chemistry models,comprehensive material science, electro-mechanics analyticaltools and improved materials processes. Efforts are needed to gaina better understanding of the parameters that control the EAPelectro-activation force and deformation. Construction of mobilityor articulation system that is actuated by EAP requires componentsas shown in Fig. 20(i-Right) as a block diagram. While each ofthe listed components is at various advanced research phases,EAP actuators are the least developed technology and extensiveeffort is required to bring it to a mature stage. On the otherhand, EAP materials reach their elastic limit at lower stress levelscompared to EAC, and their actuation stress falls far shorter thanEAC and SMA actuators. Furthermore, in Fig. 9-(ii), a table ofcomparison of properties of some actuation materials are alsolisted are compared. In this table a comparison is given betweenEAP, EAC and SMA. From the table, it is very obvious to see theproperties in which EAP offer superior capability.

The most attractive feature of EAPs is their ability to emu-late biological muscles offering resilience, toughness, large ac-tuation strain and inherent vibration damping. This similaritygained them the name ‘‘Artificial Muscles’’ with the potentialof developing biologically inspired robots. To observe the de-formation properties, in Fig. 9-(iii), middle, they have shownan electrostrictive grafted elastomer based bimorph actuatorin an unexcited state, also they show one direction excitedstate (left), and opposite direction excited state (right). EAP canpotentially afford further lifelike aesthetics, such as vibration and


Fig. 8. Memory alloy-based tendon-driven actuation, [15]. (i) (Left): Anatomy of the index finger. (i) (Right): Artificial finger test-bed with six tendon cables routing throughthe finger core and attached to the corresponding six remotely placed SMA actuators. (ii): Comparison between reference and actual minimum jerk flexion trajectories ofartificial finger’s MCP using Visualeyez motion tracking system. (Left): Placement of LEDs for Visualeyez system. (Right): Closed-loop MCP joint following minimum jerkflexion trajectory.

shock dampening, and more flexible actuator configurations. Fur-ther to this, EAP materials can add extra technological advance-ment to robotics. They can be used to composemechanical devicesand robots with no traditional components like gears, and bear-ings, which are responsible to their high costs, weight and prema-ture failures. EAP can produce large displacement, while using lowmass, low power and, in some of these materials also low voltage.These characteristics make them attractive actuators for roboticsapplications. Capabilities of EAPs to emulatemuscles offers roboticcapabilities that have been considered far away of implementation,once relying on existing actuators. Exploiting the properties of arti-ficial muscles may enable even the movement of the covering skinto define the character of the robots and perform expressivity. Fi-nally, in Fig. 9-(iv), a conductive EAP actuator is being shown asbending itself, under stimulation of (2 V, 50 A) excitation. Being ei-ther to the left or right, depending on the current direction, strongor week, depending on the current strength. Such bending proper-ties of such materials has made them very attractive for roboticsuse, and in particular for further studies on the area of artificialmuscles for actuation.

3.4. Dexterous robotics hands actuated by electro-active polymers,(building a robot hand)

In [19], Cohen Y., stated that the capability of EAP materialsto emulate muscles offers robotic capabilities that are still in therealm of science fiction when relying on existing actuators. Thelarge displacement that canbe obtainedusing lowmass, lowpowerand, (in some of the EAPs), also low voltage, makes them attractive

actuators for robotics use. In this respect, of an application, at JPL,EAP actuators that can induce bending and longitudinal strains,were used to design and construct aminiature robotic arm, refer toFig. 10. This robotic arm illustrates some of the unique capabilityof EAP, where its gripper consisted of four bending type EAP fingerstrips with hooks at the bottom emulating fingernails and it wasmade to grab rocks similar to human hand.

To encourage the development of effective Electro-ActivePolymers actuators for robotics applications, which will make animpact towards the future of robotics, toy industry, animatronicsand others, two platforms were developed and are now availableat the Jet Propulsion Laboratory (JPL). One of two platforms isan ARTIFICIAL HAND. At present, conventional electric motors areproducing the required deformations to make relevant movementof the robotics hand. The new JPL robotic hand is equipped withtandems and sensors. This allows the functioning and movementsof the various jointsmimicking humanhand. The hand index fingeris currently being driven by conventional actuators (motors). Thisallows to serve as a baseline, hence they will be substituted byEAP actuation when such materials are developed as effectiveactuators, [19]. Furthermore, in reference to [19], Bar-Cohen Y.,pointed that Electro-Active Polymers canbedivided into twomajorcategories based on their activationmechanism including ionic andelectronic, refer to Fig. 10-(ii). Coulomb forces drive the electronicEAP, which include electrostrictive, electrostatic, piezoelectric andferroelectric. This type of EAP materials can be made to hold theinduced displacementwhile activated under aDC voltage, allowingthem to be considered for robotic applications. Such EAPmaterialshave greater mechanical energy density and they can be operated


Fig. 9. Artificial muscles: electro-active polymers, [16]. (i)-(Left): EAP infrastructure and areas needing attention. (i)-(Right): A schematic diagram of the basic componentsof an EAP-driven system. (ii): Table of Comparison of the properties of some actuation materials, [17]. (iii)-(Left): One direction excited state. (iii)-(Middle): Electrostrictivegrafted Elastomer based bimorph actuator in an unexcited state, (iii)-(Right): and opposite direction excited state. (iv): Conductive EAP actuator is shown bending understimulation of (2 V, 50 A), [18].


Fig. 10. Dexterous robotics hands actuated by electro-active polymers, [19]. (i): Finger EAP gripper lifting a rock. Some of the unique capability of EAP. Gripper consisted offour bending type EAP finger strips with hooks at the bottom emulating fingernails and it was made to grab rocks similar to human hand. The miniature robotic arm usingEAP actuators to provide the lifting/dropping of the arm and manipulate the gripper fingers. (ii): List of the leading Electro-Active Polymers materials.

in air with no major constraints. However, the electronic EAPrequire a high activation fields (> 100 → V/µm) that may beclose to the breakdown level.

3.5. EAP electro-active polymers for robotics hand actuation: makingrobots actuated by artificial muscles

To make use of Electro-Active Polymers as actuator for multi-fingered robotics hands, G. Whiteley (Sheffield Hallam University)have constructed a robotics hand driven by such promisingmaterials. The hand was equipped with an actuator which wasinvented by G. Pioggia, from UNIVERSITY OF PISA, in Italy. Moredetails on this can be found also in [20]. Refer to Fig. 11 forillustration. To stimulate a global research interest in Electro-Active Polymers actuators, Bar-Cohen [20], have posed an ongoingchallenge few years ago to scientists and engineers worldwide.The main challenge objective was focused in such a way to see ifanyone could develop a robotic arm driven by artificial muscles.This challenge requires tackling the problem on all its fronts-from fundamental science and engineering, to robotic controland artificial intelligence. Even though such a challenge has notyet been fully met, scientists have made progress in findingways to control a robotic arm with artificial muscles. In addition,researchers are now working hopes to see technology that willcombine artificial muscles with prosthetics and allow disabledpeople to perform physical tasks independently. Scientists andengineers world-wide are now joining combined effort to makeeffective Electro-Active Polymers actuators the activators of choicein future devices and mechanisms.

There are a number of new challenges in terms of using EAPfor actuating robotics hands. The new technology has a number ofexcellent potentials, however, there are also a number of importantissues to be looked into. One of such issues is the gripping power ofsuch materials, since, studies do show a soft level of gripping onceused for object lifting and movements.

3.6. Shadow dexterous muscle hand air muscle robotics handactuation

Shadow hand dof and movements abilities: The Shadow DexterousHand [21], is also another attempt to get closer to bio-inspiredrobotics hand. It was designed in such away to have a range

of movement equivalent to that of a typical human being hand.In this respect, four fingers of the SHADOW hand, contain twoone-axis joints connecting the distal phalanx, middle phalanx andproximal phalanx and one universal joint connecting the finger tothemetacarpal. Furthermore, the little finger has an extra one-axisjoint on the metacarpal. This is to provide the hand with a palmcurl movement, as closed to the human being hand. For the thumbstructure, it contains one one-axis joint connecting the distalphalanx to the proximal phalanx, one universal joint connectingthe thumb to the metacarpal and one one-axis joint on the bottomof the metacarpal to provide a palm curl movement. The wristcontains two joints, providing flex/extend and adduct/abduct. Thismeans that the SHADOW dexterous hand has (24 rotary joints)all together, with 20 degrees of freedom. The hand total degreesof freedom, gives the hand more articulation, where movementsare achieved as the hand contains an integrated bank of 40 airmuscles actuating 24 joints. This is also allowing a direct mappingfrom a human to the robot. The fitted muscles are compliant incharacter, this would allow the hand to be used around soft orfragile objects. In terms movement and force control, the handhas integrated sensing and position control, using joint positionand muscle pressure sensors to facilitate precise control fromoff-board computers, even it can be integrated into any existingrobotic arm platform. This is depicted in Fig. 12. The SHADOWHand system incorporates all necessary control systems (softwareprovided under GNU GPL) and documentation for research andteaching purposes, and fully integrates with the Robot OperatingSystem ‘‘ROS’’. In order for the hand to have precise control and toincrease its level of interaction with its surroundings, the hand canbe fitted with a number of touch sensing options.Shadowhand sensing: The ShadowHand comeswith analog contactsensing regions on all fingertips. And it takes an open approachto fingertip sensing. As standard, the Dexterous Hand comesfitted with a PST contact sensor. We partner with (SynTouch LLC)to integrate their remarkable (Syntouch BioTac) sensor on theHand. The standard PST tactile sensors is intended to provide asimple, robust, low-cost sensing solution for detecting contact.They provides accuracy of (0.5N across a 0 → 10N) range allowingthe user to measure relative forces and to indicate contact. Forsensing joints rotations, a hall effect sensor measured with typicalresolution 0.2 degrees. Such analog data is sampled locally using a12-bit ADC’s. A typical sampling rate, can go up 180 Hz.


Fig. 11. Dexterous robotics hands actuated by EAP, electro-active polymers, [20]. (i): EAP actuated finger link. (ii): Artificial hand test bed to be equipped with tandems andsensors for the operation of the various joints mimicking human hand. The arm was made by G. Whiteley (Sheffield Hallam University), U.K.

Fig. 12. The SHADOW robot hand, [21]. (Left): Shadow muscle hand (integrated system). (Middle): Shadow hand kinematics configuration. (Right): Comparison of theShadow dexterous hand with a human hand.

Fig. 13. Details of shadow hand air muscle, [21]. Once actuated with a supply of compressed air, shadow hand air muscle contract by up to 40% of its original length.

In terms of hand actuation, the SHADOW hand is drivenby up to (40) air driven muscles mounted on the forearm, asaway from the fingers, Fig. 13. For details of how the air driven

muscles is operating, Fig. 13 shows some more details of howthe muscles is reshaping to actuate the hand. These providecompliant movements, [21]. Following the biologically-inspired


design principle of the hand design, tendons couple the airmusclesto the joints. Integrated electronics at the base of the hand systemdrive the pneumatic valves for each muscle and also managecorresponding muscle pressure sensors. There are two modes ofactuation that are used for the hand control, in addition, twoopposing muscles permit full control and variable compliance ofthe movement for most joints. To produce human movementcharacteristics, coupled sort of drive is used for the Middle andDistal phalanges of the fingers. For control purposes, the pressurein each muscle is sensed by a solid-state pressure sensor. This ismounted directly on the valve manifold, and measured with 12-bit resolution across the range of (0–4) bar. In [21], it was reporteda number of advantages of employing such type of air muscles.They are lightweight air muscles, where the weigh as little as 10grammes. In particular, they are particularly useful for weight-critical applications. They are also lower cost muscles, and cheaperto buy and install than other actuators and pneumatic cylinders.They are also smooth and have no ‘stiction’ and have an immediateresponse. Thiswould result in smooth andnaturalmovement. Theyare also flexible air muscles, and can be operated when twistedaxially, bent round a corner, and need no precise aligning. Alsothey are characterized to be powerful air muscles, with an abilityto produce an incredible force especially when fully stretched.

Furthermore, they are damped air muscles, this gives thema character of being self-dampening when contracting (speed ofmotion tends to zero), and their flexible material makes theminherently cushionedwhen extending. In terms of hand electronicsand control, there are up to (7) analog to digital convertersdistributed across the palm. This should be providing up to (26)active 12-bit sensing channels. There are also a number of valvesdriver nodes at the base of forearm incorporating per-musclepressure sensing and providing timed and PID control. The valvedriver boards implement PID control of individual valves. Thiscontrol can be flexibly configured to take set point and targetdata from a variety of sources. Such controllers can be configuredvia the standard robot interface and appropriate programs, scriptsand graphical examples of this are provided. A standard X-86-compatible PC (VIA Mini-ITX: others by arrangement) runningDebian GNU/Linux with the RTAI real-time. This can be used forinitial set up, evaluation and operation, as well as serving asa template for your own control system. A dedicated PC usedand is fitted with an external CANBUS interface. Software inthe host PC provides sensor calibration and scaling, mappingsfrom sensor names to hardware and permits easy access to allrobot facilities from C code, shell scripts, or GUI. Microcontrollers(PIC18F4580) micros are used for embedded control throughoutthe robot system. The firmware is provided as source on the hostPC. Allmicrocontrollers are connected to the robot CANBUS. In fact,the Shadow hand is also another attempt to emulate a human-likehand. The use of air-driven artificial muscles to actuate the handfingers, gives the hand a momentum to be an excellent front forfurther research in using muscles for hands actuation.

3.7. Ionic polymer–metal composites (IPMCs) as biomimetic sensors,actuators and artificial muscles

For a purpose of employing Smart Materials and Structures,such as Ionic polymer–metal composites, for sensing and actuationpurposes, and robotics use in particular, a research was alsoconducted by Shahinpoor et al. [22], throughwhich they presentedan introduction to ionic polymer–metal composites and somemathematical modeling pertaining to them. In this sense, they alsofurther discussed a number of recent literatures and results inconnectionwith ion-exchange polymer–metal composites (IPMCs)as biomimetic sensors and actuators. Some theoretical modelingon the mechanisms of sensing and actuation of such polymer

composites were also discussed. They stated that, strips of suchcomposites can undergo great bending and flapping displacement,once an electric field is imposed across their thickness, thus, inthis sense they are large motion actuators that useful for roboticsactuation purposes. Such a behavior can be depicted in Fig. 14. Herein Fig. 14-(left), it shows a typical linear-type robotic actuatorsmade with IPMC legs, whereas in Fig. 14-(right), it shows an IPMCactuator response for square and sawtooth wave input at (2.5)Volts rms and a current of about 20 milliamps.

Shahinpoor et al. [22] have also reported that, it was also foundthat, conversely by bending the composite strip, either quasi-statically or dynamically, a voltage is produced across the thicknessof the strip. Hence, they can also be enrolled as large motionsensors, where output voltage is to be calibrated for a standardsize sensor and correlated to an applied stresses. Shahinpooret al. [22] have also presented anew type of soft actuator andmulti-fingered robotic hand were made from IPMC artificial musclesand reported that to be quite superior to conventional grippersand multi-fingered robotic hands. They have also reported that,conducted experiments confirmed that such types of compositemuscles show remarkable bending displacement that follow inputsignal very closely. It was also found that once an appliedsignal frequency is changed, different displacement is produced,where to a point where large deformations were observed at acritical frequency, when maximum deformation was observed,beyond which the actuator response was diminished. Shahinpooret al. [22] concluded that, the observed remarkable vibrationalcharacteristics of (IPMC) composite artificial muscles clearly pointto the potential of thesemuscles for biomimetics applications suchas swimming robotic structures, wing flapping flying machines,slithering snakes, heart and circulation assist devices, peristalticpumps and dynamic robotic cilia-worlds. The fact that they stilloperated at very low temperatures such as — (140 °C) showstheir potential as cryogenic sensors and actuators. Their resistanceincreasedwith decreasing temperature, a property that is oppositeto all metallic conductors, [22].

4. Bio-inspired robotics hand tactile sensing

Throughout literature, tactile sensing is considered as animportant integral part for robotics use and applications. It isdescribed as the detection andmeasurement of contact parametersin a selected area. It has its use in robotics, haptics, rehabilitationandmany other applications. Among some others, tactile sensing isalso needed for exploration and precisemanipulation of real worldobjects. The approach followed is, once a robotic hand interactswith a grasped object, is an important issue to sense the real-worldobjects rich physical parameters. A typical interaction behaviorsdepends on how heavy and hard a grasped object is, once held,how the surface feels once brought in contact, how it deforms oncontact, and how it moves when pushed. In this respect, withinthis section,we look at some recent developments in terms of handtactile sensing, that are useful for articulated robotics hands.

4.1. Bio-inspired adaptive grasping by multi-fingered hand withtactical sensing capabilities

Takahashi T. et al. [23], have proposed a new robust forceand position control method for property-unknown objects duringgrasping. The proposed control technique is capable of selectingthe force control or position control, and smooth and quickswitching according to the amount of the external force. Theproposed method was also applied to adaptive grasping by three-fingered hand which has a total of (12) DOF. This is shown inFig. 15, where the experimental results revealed that the smoothcollision process and the stable grasping is realized even if the


Fig. 14. Ionic polymer–metal composites (IPMCs), [22]. (Left): A typical linear-type robotic actuators made with IPMC legs. (Right): IPMC actuator response for square andsaw tooth wave input at (2.5 V rms) and a current of about 20 milliamps.

Fig. 15. Bio-inspired adaptive grasping by multi-fingered hand, [23]. (i)-(Left): Appearance of three-fingered hand. (i)-(Right): The force and position controller for theadaptive grasping. (ii)-(Left): Fingertip tactile sensing mechanism. (ii)-(Right): Boiled egg (42 mm, 54 g, soft), and a Raw egg (43 mm, 63 g, hard).

precise surface position, the mass and the stiffness are unknown.In addition, a new algorithm determines the grasp force accordingto the slippage, where ‘‘slip’’ was measured with the tactile sensorand the viscoelastic media on the fingertip.

Takahashi T. et al. [23] have reported that, the proposedmethodwas applied to the grasp control. Even if precise surface position,mass and stiffness are unknown, the proposed method makesstable grasp motion. Such an attempt and experimental setupshows a good model for integrating tactile, force, and positioncontrol. In this respect, Takahashi T. et al. [23] have also reportedthe grasp experiments for several kinds of objectswhose size,massand stiffness are different. While being grasped, these objects aremoved by the manipulator controlled with position controller. Inthis respect, Fig. 15-(ii) shows Boiled Eggwithout eggshell and rawegg are graspedwith sameparameter for grasp control, and contactforce at thumb is (1.5 N). In the proposed control methodology, thegrasping force proportionate to mass is determined without mass-sensing, and the friction unknown object grasping was realized.

The effect of the proposed method has proved, by experiments, asmooth grasping and moving of objects using the designed smallthree fingered hand.

4.2. A robust, low-cost and low-noise artificial skin for human-friendly robots

Bio-inspired tactile sensing has also received a substantialamount of research recently. In their recent novel researchframework at the Center for Design Research, Stanford University,Ulmen, J. and Cutkosky, M. [24], have introduced, a design anda test of a mechanically robust, low-noise, scalable capacitiveforce sensing array, that can used easily as a tactile sensing forrobotics hands. The setup shown in Fig. 16. The use of shieldingaround, and as part of the sensor, minimizes electrical noise andstray capacitance from coupling into the sensor signal. UlmenJ. and Cutkosky M. have indicated that, by placing individual


Fig. 16. Robust, low-cost and low-noise artificial skin for human-friendly, [24]. (Left): A single sensing circuit. (Middle): Physical tactile sensing layout. (Right): Tactiledisplay recorded from the 4 × 4 capacitive array prototype.

(Left) (Right)

Fig. 17. Biomimetics and dexterous manipulation Lab, Stanford University, [25]. (Left): Finished and installed sensor suite on the Robotic Adaptive Gripper. (Right): CADrendering of the hand and sensors showing taxel locations.

small and low-cost processing circuits at each taxel, this allowsimmediate digitization of signals, further reducing noise coupling.Also it was reported that, interfacing with a large taxel arrayrequires a minimal number of wires. With a serial addressing ofmemory technology case, a (100) sensor array could reasonablybe scanned at (1 kHz) rate with only five interface wires. Thesensor is also constructed from physically rugged materials yet issoft enough to allow energy absorption in a collision. Ulmen, J.and Cutkosky, M. [24] have also reported that, with its low-cost and high scalability, this sensor design makes whole bodysensor arrays feasible. The tactile sensing arraywas presentedwithapplication as a whole body artificial skin covering. Such a highlyscalable design provides excellent noise immunity, low-hysteresis,in addition, it has the potential to be made flexible and formable.

The presented tactile array in [24], was useful for roboticsand dexterous hand applications. In this sense, the UNIVERSITYOF STANFORD [25], has reported a application of such tactilesensing for dexterous robotics hands. The work was focusing ongrasping and manipulating blind, using only tactile feedback fromsuch sensors. The various sensors are processed to determinetactile events, such asMAKING/BREAKING contact or SLIP betweenthe hand and grasped objects, as well as CONTACT LOCATIONand STATIC PRESSURE. Such grasping events, and the continuouscontact information can then be used to control more complexmanipulations. For validation purposes, few experiments were

conducted also. In this sense, Stanford University biomimeticsand dexterous manipulation laboratory, have taken a further stepby building a custom tactile sensor suite for a multi-fingeredrobot hand, which is attached to an AdeptOne, (5-DOF), SCARAindustrial robot arm. The Stanford University multi-fingered robothand, as equipped with tactile sensing. This is shown in Fig. 17.The hand is an example of an under-actuated hand with threethree-phalanx fingers. For actuation, each finger has a singlemotor to open/close it, with a spring-loaded transmission linkage.This would allow the fingers to wrap around large objects andpinch smaller objects. A fourth motor controls the spread ofthe index/middle fingers and can be used to pinch very smallobjects between them. In [25], furthermore, for the tactile sensorsuite, they have designed an adaptive gripper covers, virtuallyevery surfacewith normal-pressure-sensing taxels. In addition, thesensor on the distal phalange of each finger is equipped with a 3-axisMEMS accelerometer to provide dynamic data at the fingertipsat a rate of 800 Hz, [25].

Biomimetics and dexterous manipulation Laboratory, at Stan-ford University, has also reported that, there are a total of (132)pressure taxels in the suite, [25]. On average they are (1.5 →

2 cm2) with (1 → 1.5 cm) of an area center-to-center spacing,although their size and shapes vary. Such details can be seen in theCAD rendering, Fig. [17]. Since they are hand assembled the sen-sitivity varies, but the most sensitive taxels can distinguish forces


on the order of 20 mN (∼2 g weight) and reach a saturation underforces in the (100 N) range. The hand tactile sensors are sampled ata rate of (100Hz). Thiswill be allowing sensors to have a bandwidthof (50 Hz), as this considered well within the range of ‘‘fast acting’’human tactile sensors. In terms of hand dynamics and control, itwas also reported by the Biomimetics and dexterous manipulationLaboratory that,WillowGarage’s ROS frameworkwas employed forcontrolling the arm, hand, and tactile sensors all together. Willow-Garage’s ROS have even put and coded custom hardware interfacenodes for all three fingers. In a general sense, the development ofthis class of tactile sensing gives a good degree of indication, inwhich direction advanced tactile sensing is heading now. Effortsby Ulmen, J. and Cutkosky M., [24], can be considered as the mostcomprehensive research in terms building the right sensing capa-bility for robot hands.

4.3. Cutkosky categorization of hand grasps, leading to new technolo-gies for force and tactile sensors

For a purpose of building a more knowledgeable system,late of 1980’s, a comprehensive study by Cutkosky, M. [26]was conducted, where he described a theme for automatedrobotics grasping. He indicated that, while studying grasping andmanipulation, it was found that, there are two very dissimilarapproaches to the matter. (i) (A Knowledge-Based Approaches),which is based primarily on empirical studies of human graspingand manipulation, and (ii), (Analytical Approaches), as basedprimarily on physical models of the manipulation process. Healso indicated that, the current analytical models of graspingand manipulation with robotic hands contain simplificationsand assumptions that limit their application to manufacturingenvironments. For the purpose of evaluating these models, a studywas undertaken of human grasps used by machinists in a smallbatch manufacturing operation. Based on that particular study, acategorization of graspswas constructed. Such a categorization canbe seen in Fig. 18. An expert system was also developed to clarifythe issues involved in human grasp choice. Comparisons of thegrasp taxonomy, the expert system, and grasp-quality measuresderived from the analyticmodels reveal that the analyticmeasuresare useful for describing grasps in manufacturing tasks despitethe limitations in the models. In addition, the grasp taxonomyprovides insights for the design of versatile robotic hands formanufacturing, [26].

Therefore, the study Cutkosky, M. [26], begins with a reviewof studies of human grasping, in particular our development ofa grasp taxonomy and an expert system for predicting humangrasp choice. These studies show how object geometry and taskrequirements (as well as hand capabilities and tactile sensing)combine to dictate grasp choice. The study by Cutkosky alsoconsidered analytic models of grasping and manipulation withrobotic hands. To keep the mathematics tractable, these modelsrequire numerous simplifications which restrict their generality.Despite their differences, the two approaches can be correlated.This provides insight into why people grasp and manipulateobjects as they do, and suggests different approaches for roboticgrasp and manipulation planning. The results also bear upon suchissues such as object representation and hand design.

Within this review, the most useful contribution of the studyof human grasps, from the standpoint of designing and controllingrobot hands, has been a better appreciation of how task require-ments and object geometry combine to dictate grasp choice. Thestudy has also resulted in a grasp taxonomy, which makes it possi-ble to identify particular grasps and to trace how they derive fromgeneric grasp types. The fact that both task requirements and ge-ometry are important is clear from everyday experience.

Table 1Biomimetic control, [46].

Intention↓ Site→ T I L

T TT TI TLI IT II ILL LT LI LL

4.4. A recent classification of force and tactile sensors techniques

In continuation to Cutkosky, M. [26], almost twenty years,in year 2008, Cutkosky M. and Howe R. has also extended thework towards hand sensing and presented a more comprehensivestudy about various efforts for designing tactile sensing, asdocumented in ‘‘Handbook of Robotics-2008’’, Cutkosky M. andHowe R., [27]. The study has provided an overview of FORCEAND TACTILE sensing, with the primary emphasis placed ontactile sensing. The study began by presenting selected basicconsiderations in choosing a tactile sensor and then review ofa wide variety of sensor types, including proximity, kinematic,force, dynamic, contact, skin deflection, thermal and pressuresensors, as given in [27]. They also reviewed various transductionmethods, appropriate for each general sensor type. The study alsoconsidered interpretation of tactile information, hence describethe general problems and present two short illustrative examples.First, this involves intrinsic tactile sensing, i.e. estimating contactlocations and forces from force sensors. Second, this involvescontact pressure sensing i.e. estimating surface normal and shearstress distribution from an array sensors in an elastic skin. Thestudy also concluded with a brief discussion of the challengesthat remain to be solved in packaging andmanufacturing damage-tolerant tactile sensors. Furthermore, Cutkosky M. and HoweR., [27], stated that, from the standpoint of hand design, it wasfound that, although the expert system contains a great deal moreinformation than can be represented in a taxonomy, the taxonomyremains useful as a design aid since it allows one to see very quicklywhere a particular grasp resides in the space of possible grasps.

Cutkosky M. and Howe R., [27] have concluded that, whilechoosing tactile sensors for a robot arm or hand, it is effective tobegin with a consideration of which tactile quantities are mostdesired and for what purpose. For example, the main concern isto obtain accurate measurements of (loads) or (contact forces)at sufficient data rates for force servoing, then (intrinsic tactile)sensing may make the most sense. If manipulating objects withsoft contacts and with sliding or rolling, curved array sensors formeasuring pressure distributions, or perhaps local skin deflections,may be desirable. This can be seen in Table 2. Cutkosky M. andHowe R., [27] have also reported that, concluded that, if exploringobjects to learn about their texture and material composition,dynamic tactile sensors and thermal sensors may be effective. Thiscan be seen in Table 2. In an ideal world, one would incorporateall these tactile sensors in a robotic end-effector without regardto cost, signal processing or wiring complexity. Fortunately, thecost and size of transducers suitable for tactile sensing aresteadily dropping and the ability to perform localized processingis improving with surface-mounted devices on flexible circuits. Inthe near future it will be increasingly possible to fabricate densearrays of transducers in-situ on contoured surfaces, using materialdeposition and lasermachining techniques. In thisway, robotsmayfinally start to approach the tactile sensitivity and responsivenessof the simplest of animals (Table 2).

4.5. Bio-inspired tactile sensing arrays

In [29], Ravinder D. also presented another experience in adevelopment of tactile sensing arrays. The designwas also inspiredby cutaneous sensing in humans, for the fingertips of a humanoid


Fig. 18. Cutkosky, an early and formal way to categorize grasps, [26].

Fig. 19. Contact switch arrays fabricated from flexible printed circuits., [29]. (Left): A functional comparison of touch sensors and the receptors in the skin. (Right): (a) 32taxel MEA test chip (left) with enlarged view of a taxel (right); (b) Front and backsides of MEA test chip epoxy-adhered with 50 µm PVDF-TrFE film (left); Packaged MEAchips (right).

robot. This is illustrated in Fig. 19. The tactile sensing arrays andarrangements have been developed in two phases. Microelectrodearrays (MEA), having up to (32) sensing elements — each epoxyadhered with (25 µm) thick piezoelectric polymer (PVDF-TrFE)film,were fabricated in the first phase.When connected to the gateof FET devices (external to the chip), each element onMEA acts likean extended gate; thereby facilitating modulation of charge in theinduced channel by the charge generated in PVDF-TrFE film — as aresult of applied force. By such transduction action, each sensing

element converts force into voltage, as shown in Fig. 19-(left).The tactile sensing arrays developed in second phase work on thesame principle, but are free from any extended gate. These arrays(having 25 sensing elements) use POSFET (Piezoelectric OxideSemiconductor Field Effect Transistors) touch sensing elements,in which, piezoelectric polymer film is directly spin coated onthe gate area of the FET devices. Thus, a POSFET touch sensingelement ‘senses and partially processes at same site’ as is done byreceptors in human skin. It was found that, the spatial–temporal


Table 2Tactile sensor modalities and common transduction types, [27].

Sensormodality

Sensor type and attributes Advantages Disadvantages

Normalpressure

1 Piezoresistivearray

Piezoresistive array

Array of piezoresistive junctions Simple signal conditioning Temperature sensitiveEmbedded in a elastomeric skin Simple design FrailCast or screen printed Suitable for mass production Signal drift and hysteresis

2 CapacitiveArray

Array of capacitive junctions Good sensitivity Complex circuitry

Row and column electrodes separated byelastomeric dielectric

Moderate hysteresis,depending on construction

3 PiezoresistiveMEMSArray

Silicon micro-machined array with dopedsilicon strain gauged flexures

Suitable for mass production Frail

4 Optical Combined tracking of optical markers with aconstitutive model

No interconnects to break Requires PC for computing appliedforces

Skindeformation

5 Optical Fluid-filled elastomeric membrane Compliant membrane Complex computations Hard tocustomize sensor

Tracking of optical markers inscribed onmembrane coupled with energy minimizationalgorithm

No electrical interconnects tobe damaged

6 Magnetic Array of hall effect sensors Complex computationsHard to customize sensor

7 Resistivetomography

Array of conductive rubber traces as electrodes Robust construction Ill-posed inverse problems

8 Piezoresistive(Curvature)

Employs an array of strain gauges Directly measure curvature Frailty of electrical interconnects

Hysteresis

DynamicTactileSensing

9 Piezoelectric(stressrate)

PVDF embedded in elastomeric skin High bandwidth Frailty of electrical junctions

10 Skinacceleration

Commercial accelerometer affixed to robot skin Simple No spatially distributed Content

Sensed vibrations tend to bedominated by structural resonantfrequency

performance of these chips is similar to that of skin in the humanfingertips.

The study has also exposed a linear response over range offorces (0.15 → 5 N), which is much wider than the forces ex-perienced by humans in normal manipulative tasks. In additionto sensing and processing at same site and the improved perfor-mance, the POSFET devices as an integral ‘‘sensotronic’’ unit offerpractical advantages. Example of which, is reduction in number ofwires, which is a key issue in robotics. This is shown in Fig. 19-(right). The performance, utility and local processing capability ofPOSFET touch sensors can be further improved by including com-plex circuitry and following a System on Chip/System in Packageapproach. Though primarily designed for robotic applications, dueto the fact that impedance of PVDF-TrFEmatches well with humantissues, these devices can also be suitable for various medical ap-plications.

4.6. A biologically inspired tactile sensor array via phase-basedcomputation

Another approach based on computation of phase was intro-duced by Cassidy A. and Ekanayake V., [30]. They show a newmechanism for tactile sensing. They have proposed a biologicallyinspired tactile sensor array based on neural spike processing.Asynchronous data processing is integrated on-chip with the sen-sor array and converts the high-bandwidth raw input data intohigher-level information with lower bandwidth requirements. Us-ing PHASE-BASED computation primitives, as opposed to tradi-tional rate-based neural spike codes, the array can compute the

point-of-contact, force magnitude, force direction, and the pres-ence or absence of slips, all in real time. Simulations demonstrate areduction in the number of computations by up to three orders ofa magnitude over a comparable synchronous approach. In this re-spect, Fig. 20-(i) shows a schematic diagram for sensor single mo-dem, the associated block diagram for the entire tactile sensor. Itshows a front-end consisting of a pressure sensitive array that gen-erates binary force events corresponding to spikes in a biologicalsystem, [30]. In Fig. 20-(i), we also show how an individual sensornode generates a spike due to a threshold crossing. As describedin Cassidy A. and Ekanayake V., [30], phase-based encoding mapsnaturally to an event-driven computation approach, since compu-tation only occurs when spikes are observed. The entire systemblocks are shown in Fig. 20-(i). The system has a front-end consist-ing of a pressure sensitive array that generates binary force eventscorresponding to spikes in a biological system.

The novel idea behind the presented design, is that it is, anintelligent tactile sensor array, that it can accurately measure themagnitude and direction of incident force, as well as determineif a slip has occurred. The computation in the array is basedon the behavior of the FA-I afferents and time-to spike, thusencoding of neural spikes in the human somatosensory system.Cassidy A. and Ekanayake V., [30], also simulated the systemusing asynchronous logic, and demonstrated the efficiency of time-domain-based computing primitives. In future work, the plan toquantify the accuracy of the system, with regard to jitter fromunmatched circuit delays. It is intend to update the slip detectionblock to include magnitude and direction of slip calculations. Also,


Fig. 20. A Biologically inspired tactile sensor array utilizing phase-based computation, [30]. (i)-(Left): Typical sensor node diagram. (i)-(Right): System block diagram. (ii)(Right): Increasing force normal to skin: (a) x = 0, y = 0, z = 2.0, (b) x = 0, y = 0, z = 4.0; Angled force acting on the skin model: (c) x = −20, y = 10, z = 4.0; (d) x = 20,y = −10, z = 4.0. (iii)-(Left): Neighbor array diagram (12 nodes). (iii)-(Right): Sensor array spike codes, (top) rate-based encoding (bottom) phase based encoding.

multiple points of force incident on the array will be accountedfor. The final research goal is to implement and fabricate the tactilesensor array as a custom integrated circuit.

4.7. Closing the loop: biomimetic sensor for control of grip (impedancechange detection)

In [31], Wettels N. et al., have developed a novel robust tactilesensor array that mimics the human fingertip and its distributedset of touch receptors, as in Fig. 21(i)-(left). The mechanical com-ponents are similar to a human fingertip, with a rigid foundationsurrounded by aweakly conductive fluid containedwithin an elas-tomeric skin, refer to Fig. 21. It uses the deformable properties ofthe finger pad as part of the transduction process. Multiple elec-trodes are mounted on the surface of the rigid core and connectedto impedance measuring circuitry within the core. External forcesdeform the fluid path around the electrodes, resulting in a dis-tributed prototype of impedance changes containing informationabout those forces and the objects that applied them. Wettels N.

et al. have also reported initial results with prototypes of the sen-sor, and proposed strategies for extracting features related to themechanical inputs and using the information for reflexive grip con-trol. The research has also observed the impedance changes oncedeformations occur around the electrode. This would demonstratethe ability to sense deformations outside the electrode’s immedi-ate vicinity. This is consistent with the hypothesis regarding theflat probe behavior previously described. Fig. 21(i)-(right), showsthat the highest impedance value was measured when the probedeflected the skin directly above the electrode of interest. As illus-trated in Fig. 21-(ii), the rollingmotion caused global deformationsthat were observable at both electrodes. This is not unlike the be-havior of the human finger pad when a human uses a precisiongrip to lift a small object. In general sense, the research has shownan ability to build a bio-type tactile fingertip sensing for fingers,that could be useful for objects dexterous manipulation. In generalsense, the presented tactile hand sensing, does provide a mechan-ically robust and informatically rich set of sensors, that bears someresemblance to the biological tactile sensors.


Fig. 21. Biomimetic tactile sensor, [31]. (i)-Left : (a) Drawing of biomimetic tactile sensor. (b) Sensor prototype core with ‘‘skin’’ removed. (i)-Right : Log impedance(1–100 k�) versus static deflection of skin applied by three probes with different curvatures. Three distinct operating regions (labeled A, B & C) are present for each curveand discussed in text. (ii): Two electrode sensor output during a roll.

4.8. Grip control using biomimetic materials for tactile sensingsystems: (triaxial force sensing)

Wettels N. et al. in [32], have also presented a proof-of-conceptfor controlling the grasp of an anthropomorphic mechatronicprosthetic hand by using a biomimetic tactile sensor, Bayesianinference and simple algorithms for that involves estimation andcontrol. The sensor takes advantage of its compliant mechanicsto provide a triaxial force sensing end-effector for grasp control,Fig. 22. By calculating normal and shear forces at the fingertip,the prosthetic hand is able to maintain perturbed objects withinthe force cone to prevent slip. Here a KALMAN filter is used as anoise-robust method to calculate tangential forces. Biologically-inspired algorithms and heuristics are presented. They can alsobe implemented on-line to support rapid, reflexive adjustments ofgrip. The approach followed by Wettels N. et al. [32], is to makea simple on-line grip control algorithm, (refer to Fig. 22-(ii) forthe proposed flowchart), that can be calculated quickly and to beconsistent with the short latencies needed in grasp managementwithout object or plant knowledge. The sensor is compliant tosupport grip and its design is such that simple algorithms canbe implemented in real-time to calculate normal and tangentialforces regardless of point of contact. To convert force to voltages,Wettels N. et al. developed a simple state estimation using K-Filter,with initial state being zero tangential force when no forces arebeing applied. A Kalman filter was chosen, because this particularsensor configuration produces noisy signals and K-Filter filters actas low-pass filters. This sensor noise has since been mitigated byrefining the texture applied to the internal surface of the skin.Furthermore, the K-Filter integrates signals from a population ofsensors to a produce a force output. This is necessary because thevoltage to force relationship calculation cannot be direct like the

normal force. Fig. 22 shows a typical normal force sensor versesforce-plate output after calibration trials.

4.9. Biomimetic sensing for robotics manipulation: nonholonomicconstraints (lie brackets approach for nonholonomic grip constraints)

In manipulation tasks by articulated hands, we need to con-sider that, humans have the advantage over machines due to anunparalleled ability to process information from various inputs,including touch. A set of four robot end-effectors was equippedwith (force sensors) to provide haptic feedback to aid in perform-ing the manipulation tasks of rotating a sphere and a cube, waspresented by Petroff N., [33]. The motion planning algorithm usedto compute the robots’ joint angles is known as (Steering-Using-Piecewise-constant-inputs) and is applicable to under-actuated,nonlinear, nonholonomic, driftless systems. Nonholonomic con-straints arise during contact, requiring the fingers to only roll rel-ative to the object without slippage. However, the algorithm givesrise to new Vector Fields called {Lie Brackets} that allow the fin-gers to be reconfigured without releasing the object, effectively in-creasing the workspace of the manipulation system. Experimentswere conducted with fixed-point manipulation to produce a base-line for comparing reconfigurablemanipulation experiments, referto Fig. 23. Both (open loop) and (closed loop), reconfigurable ma-nipulation experimentswere conducted on a spherical object, as inFig. 23-(Middle). Three geometric parameters are then be used todefine the contact evolution equations. This is expressed in termsof geometry as in Eq. (1):

α̇f = M−1f

Kf + K̂o

−1

−ωy+ωx

− K̂o

υxυy


Fig. 22. Biomimetic tactile sensing systems, [32]. (i) (Left): Otto bockM2 hand. (i) (Right): TAC prototype sensor array. (ii) (Left): Grip adjustment algorithm flow chart. (ii)(Right): Normal force sensor vs. force-plate output after calibration trials.

Fig. 23. Biomimetic sensing, [33]. (Left): Robotic fingertip sensors. (Middle): Beginning and ending configuration of ball under fixed-point rotation about its z-axis. (Right):Schematic of robotic manipulation test bed with reference frames.

α̇o = M−1o Rψ

Kf + K̂o

−1

−ωy+ωx

− Kf

υxυy

(1)

ψ̇ = ωz + TfMf α̇f + ToMoα̇o

υz = 0.

In conclusions, Petroff N. reported that, when starting such a classof research, it was believed tactile feedback would relax the accu-racy requirements in calibrating the zero position of the robots. Inretrospect, however, it must be concluded that calibration of thezero position is more important with tactile sensing than with vi-sion. It was concluded also that, the method used to compute theobject’s contact coordinates is a slave to the zero configuration.This is not necessarily the case for a vision system. Vision could

be used to adjust the joint angles since the actual contact coordi-nates can be known from a global frame of reference. A calibrationscheme using tactile sensors which parallels those for vision sys-tems could be developed, however.

4.10. Bio-inspired sensorization of a biomechatronic robot hand for agrasp-and-lift task (triaxial force sensor)

In [34], Edinc B. et al., have also presented a study investigatingissues related to bio-inspired systems, tactile sensory system forrobotic hand and robotic fingers. They concluded from numerous(Neuro-physiological) studies, that humans rely on detectingdiscrete mechanical events that occur when grasping, lifting and


Fig. 24. Bio-inspired sensorization of a biomechatronic robot hand, [34]. (Left): Characterization of the static and dynamic response properties of the three-axial forcesensor. (Right): Grasp-and-lifting trial.

Fig. 25. Contact switch arrays fabricated from flexible printed circuits, [28]. (Left): The three-axis force sensor (A) reference system for the sensor; (B) fabricated aluminumsensor. (Right): Contact switch array embedded in the skin of a prosthetic hand.

replacing an object, i.e., during a prototypical manipulation task.Such events represent transitions between phases of the evolvingmanipulation task such as object contact, lift-off, etc., and appear toprovide critical information required for the sequential control ofthe task as well as for corrections and parametrization of the task.They have sensorized a biomechatronic anthropomorphic handwith a goal to detect such mechanical transients. The developedsensors were designed to specifically provide the informationabout TASK-RELEVANT discrete events, rather than to mimic theirbiological counterparts. To accomplish this, they have developedtwo classes of sensors. The first is a CONTACT SENSOR that canbe applied to the surface of the robotic fingers and that showa sensitivity to indentation and a spatial resolution comparableto that of the human glabrous skin. The second is a SENSITIVELOW-NOISE THREE-AXIAL FORCE sensor that was embedded inthe robotic fingertips and showed a frequency response coveringthe range observed in biological tactile sensors. They have alsodescribed the design and fabrication of these sensors, their sensoryproperties, and show representative recordings from the sensorsduring grasp-and-lift tasks. Refer to Fig. 24-(left) for more detailsabout the characterization of the static and dynamic responseproperties of the three-axial force sensor.

It was concluded that, the proposed artificial biomimetic tactilesystem could be helpful for developing and implementing a low-level embedded controller of a biomechatronic hand. Future workwill consist in implementing algorithms for mechanical transientsand slippage detection in a real-time hand controller, merging theproprioceptive and exteroceptive information from contact sensorarrays and three-axial force sensors, and allowing investigation notonly of sequential but also parallel coordination in manipulation.This line of research will be useful not only for the design of

new biomechatronic limbs, but also improve our understandingof and allow testing of proposed biological control paradigms.In addition, Fig. 24-(right) shows grasp-and-lifting trial. (a) Thebiomechatronic hand was controlled to grasp the cylindricalobject, and after a few seconds delay, the handwas lifted verticallyat (25 mm/s), held in position for (2 s), and then again lowered tothe support whereupon the grasp was released. Both a slip thatoccurred during the start of the vertical movement (b) and thetransient deceleration indicating the beginning of hold phase (c)were clearly discernable in the recorded force signals and correctlydetected over 10 ms, Edinc B. et al. [34].

The physical characterization and fabrication process of thework presented in, Edinc B. et al., [34], was already put forwardby Edinc B. et al., [28]. In reference to Fig. 25, it shows the initialresearch work that was conducted towards building such tacticalsensing. The Triaxial Force Sensor was designed to detect the threecomponents of the contact force occurring at the finger-objectinterface. Moreover, the sensor was designed to detect the contactbetween the object and the environment and to be sufficientlysensitive to sudden surface tangential force changes in order todetect slippage. Edinc B. et al., [28] have also reported that, theforce sensor was dimensioned by means of FEA tools (ANSYS 5.7)in order to identify the maximum strain levels and to choose thestrain gauge elements accordingly. The calculated maximum loadswere (4.5, 6, and 4.5 N) along the (x, y and z axis), respectively(Fig. 25). Moreover, the triaxial force sensor was calculated tosufficient bandwidth. Sensor mechanical, Fig. 25, was made of anelastic aluminum alloy. In order to detect the three components ofan applied force, a strain gauge (N3K-06-S022H-50C, VishayMicro-Measurements, Vishay Intertechnology Inc., USA) was attachedlocated to the root of each of the three tethers of the sensor(Fig. 25), Edinc B. et al., [28].


Fig. 26. Bio-inspired grasp control in a robotic hand, [35]. (Left): Tactile modules can be seen around the distal phalanges of the index and thumb fingers. (Right): Dashedline tactile stimulus, as the finger pushing oscillatory on the sensor array (intensity plot over time as recorded by the CNN chip), in correspondence of the pixel in row 21and column 24; solid line filter output (corresponding pixel brightness on the output image).

4.11. Bio-inspired grasp control in a robotic hand; massive sensorialinput

In [35] Ascari L. et al., have reported that, capability of graspingand lifting an object in a suitable, stable and controlled way isan outstanding feature for a robotics system. However, no robotictools able to perform an advanced control of a grasp as, forinstance, like the human hand does. In their research paper a(bio-inspired approach) to tactile data processing was followed inorder to design and test a hardware–software robotic architecturethat works on the parallel processing of a large amount of tactilesensing signals. They reported that; the working principle ofthe architecture bases on the cellular nonlinear/neural networkparadigm, while using both hand shape and spatial–temporalfeatures obtained from an array of micro-fabricated force sensors,in order to control the sensory-motor coordination of the roboticsystem. Few typical grasping taskswere selected, this is tomeasurethe system performances applied to a computer interfaced robotichand. Successful grasps of several objects, (completely unknownto the robot), e.g., soft and deformable objects like plastic bottles,soft balls, and Japanese tofu, have been demonstrated as well,Fig. 26-(left). Ascari L. et al. [35], have also presented a completesystem for reactive real-time safe grasp of unknown objects,based on the detection of spatial–temporal events, the core ofthe software platform being a topological analog filter, designedand implemented in a CNN processor. The proposed filter canbe completely parametrized: its sensitivity, latency, robustness tonoise, can be easily tuned to adapt to several sensors, coveringmaterials, and thicknesses. The approach could appear oversized,given the limited amount of sensors (54) exploited in this work;the intention was indeed to present and validate an architectureconceived with in mind.

Hence, performed experiments showed that all the computedfeatures are important for the task to be completed, but withdifferent weights, depending on the particular object beinggrasped. This could be seen as an indirect evidence of thedemonstrated coexistence of both bottom-up and top-downcontrol strategies in biological grasp control. Secondarily, this putsinto evidence a limitation of the current implementation, i.e. thestatic use of the tactile features, whose relative importance isnow manually selected: if an average setting is suitable for themajority of objects, particularly challenging ones such Japanesetofu require some parameters tuning to be successfully grasped. Inaddition, Ascari L. et al., [35], have presented a hardware/softwareplatform, which is considered as a biorobotic tool, allowing both toinvestigate on strategies for solving complex sensory-motor tasks.

To the validate neuro-scientific models, this requires the presenceof many sensors and biomimetic processing, as in Fig. 26-(right),hypothesizing that the very first spike in ensembles of humanskin afferents may encode complex mechanical events such as thedirection of force on the fingertip or the local shape at the fingertip-object interface.

5. Bionic and prosthetic hands

In terms of BIONIC hands, as this field of research is moving inparallel with robotics hands, the survey efforts described withinthis Section is targeting a study of BIO-INSPIRED BIONIC ANDPROSTHETIC HANDS. This is starting from an analysis of some ofstate of the art of artificial hands, designed either for prostheticsor for robotics applications. Practically, there are a number ofcommercially available prosthetic devices and human like artificialhands. Examples are Otto Bock SensorHandTM , [36], and iLIMBHand [45], in addition, to multifunctional hand designs [37–43]are far from providing the grasping capabilities of the humanhand [44]. In prosthetic hands active bending is restricted to twoor three joints, which are actuated by a single motor drive actingsimultaneously on the metacarpo-phalangeal (MP) joints of thethumb, of the index and of the middle finger, while other jointscan bend only passively. Looking at the other side of the prostheticdesigns, recently designed robotics hands have achieved high levelperformance in grasping andmanipulation. However, such designsdo make use of large controllers which are not applicable inprosthetics or humanoid robotics where it is necessary to providethe user with a wearable artificial hand. This section looks into anexample of commercially available robotics hand.

5.1. Bionic and prosthetic hands: the (ILIMB) hand

In reference to recent hand designs, literature has also revealedthat BIONIC hands have also emerged as a new frontier foradvanced research recently. Bionic hands can be used as meansto help human to restore some of grasping functionalities [45].In order to give a realistic backgrounds about Bionic hands, weshall present the design of the (iLIMB Hand). iLIMB Hand has beenintroduced lately as an specimen of such most advanced bionichand. It looks just like a real hand with four fingers and a thumb.All hand digits can be moved to permit grabbing actions. Thei-limb ultra hand is the most versatile prosthetic hand available,it is providing the ability to customize the hand for a broad rangeof activities. Two different versions of the hand designs are givenin Fig. 27.


Fig. 27. (I-LIMB ULTRA) hand, a formal way to categorize grasps, [45]. (Left): Regular size I-limb ultra hand skeleton. (Middle): Early version, the (I-LIMB). (Right): A pictureof the (I-LIMB ULTRA) hand.

Fig. 28. TAH, Tendon-activated pneumatic hand, [46]. (Left): Linear actuators providedmovement of 3 independent fingers. Fingers having≈30° of flexion, with amaximumof about 4 N of force. (Middle): Signals derived from TAP sensors, 9-s period of repetitive finger flexions. (Right): Sensitivity of TAP System. Sensitivity data was summarizedas the percentage of true positives for each diagonal sensor on each subject. Bars represent ‘‘diagonal’’ values (Di) for each subject. Sensitivity was (100%) for all subjects onat least two channels.

In reference to [45], it was reported that the hand has twomain unique features. The first feature is that, each finger isindependently driven and can articulate, as each finger is movedvia a separate motor. The second feature is that, the thumb isrotatable through (90 degrees), in the same way as the humanthumbs are. There are two electrodes that sit on the skin that pickup myoelectric signals. They are used by the computer in the backof the hand, which does two things: it interprets those signals andit controls the hand.What this translates to is thewearer being ableto control the grabbing function of their hand with the musclesin their arm making for quite a natural mechanism. Bionic handsare getting very popular, as it gives more articulation. For instant,the i-LIMB has been fitted to over (200) patients. The i-Limb Ultra.Just like the previous i-Limb Pulse, it has five individually poweredarticulating fingers and manually rotatable thumb and wrist, plusexercising strength proportional to the input signal with pulsingfor increased grip force. On top of that the device incorporates afew squeezes, such as variable grip strength for digits, improvedsoftware, and better power management.

5.2. Control of a multi-finger prosthetic hand

Available hand prostheses are either ‘body powered’, or ‘my-oelectric’ devices that restore prehension, Craelius W. et al. [46].Here, Craelius W. et al. [46], have developed a control scheme formulti-finger prosthetic hand. The prosthetic hand is controlled byextrinsic flexormuscles and tendons of themetacarpal-phalangealjoints. This is illustrated in Fig. 28. The hand uses Tendon-ActivatedPneumatic control, and has provided most subjects, including am-putees and those with congenital limb absence, control of multiplefingers of the hand.

Hand biomimetic control: A signal response matrix was generatedfor each subject, consisting of three rows, representing requestedfinger motions, and three columns, representing the three sensorlocations. This is illustrated below in Table 1: the levels of signalsreceived from the requested (diagonal) channels and the cross-talk (off-diagonal) channelswere compared. Ratios of energy levelswere expressed in decibels (dB), as computed by Eq. (2):

Rji = (10) log(Di)(Oij)

for i = 1, 2, 3 & j = 1, 2. (2)

In Eq. (2), Rji is signal energy of sensor (i) with respect to sensor(j), (Di) is the energy of diagonal sensor i, and (Oij) is the energy ofthe off-diagonal sensors with respect to each diagonal. In Table 1,(TT) represents signal energy from the hand thumb sensor for anintended thumb movement; (IT) is from the same sensor for anintended index movement. To maximize the diagonals, subjectswere instructed to use less force to help avoid cross signals. Severalresponsematrices were obtained from each patient. An example isshown in Fig. 28 (middle). All subjectswere able to produce at leastone matrix comparable to the one shown.

In conclusion, the Tendon-Activated Pneumatic Hand givesamputees control of finger flexion using natural motor pathways.Most subjects, including those with relatively short and scarredresidua, quickly gained control over several mechanical fingers.Slow typing and piano playing were demonstrated by the hand.Furthermore, beyond providing dexterity, the Tendon-ActivatedPneumatic Hand controller may facilitate the transition to morecomplete hand restorations over the future.


Fig. 29. Under-actuated prosthetic hand. Power, precision and lateral grasp, [47]. (i): System block diagram. (ii) : (A) Power grasp: all palmar surfaces of fingers are involved,and the thumb is in opposition to other fingers. (B) Precision grasp: thumb, index and middle fingertips are involved with the thumb in opposition space. (C) Lateral grasps:the thumb opposes to the volar aspect of the index.

5.3. PCA based control of a multi-DOF under actuated prosthetic hand

An under-actuated prosthetic hand controlled bymeans of non-invasive interfaces based on electromyography (EMG) was pre-sented byMatroneG. et al. [47]. Driving the designedmulti degreesof freedom (DoF) hand for achieving hand dexterity, implies to se-lectively modulate many different EMG signals in order to makeeach joint move independently. Hence, this could require signifi-cant cognitive effort to the user. A principal components analysisbased algorithm is used to drive a 16 DOFs under actuated pros-thetic hand prototype with a two dimensional control input, inorder to perform the three prehensile forms mostly used in Ac-tivities of Daily Living (ADLs). Principal components set has beenderived directly from the artificial hand by collecting its sensorydata while performing 50 grasps, and subsequently used for con-trol. This is shown in Fig. 29. (i) Power grasp: all palmary surfacesof the fingers (as well as the palm) are involved and the thumb is inopposition to other fingers. (ii) Precision grasp: thumb, index andmiddle fingertips are involvedwith the thumb in opposition space.(iii) Lateral grasps: the thumb opposes to the volar aspect of theindex, [47]. In general, principal components analysis is a mathe-matical approach, and is always used for dimensionality reduction,just inverting its algorithm and neglecting the less significant (lowweight) principal components.Whileworkingwith am-DOFs handand specific postures data set, they obtain (m) PCs constituting the(m) columns of the PCs matrix, ordered according to their weight.It was supposed that only the two first PCs are significant, two in-puts (In1 & In2), which represent the two principal hand DOFs inthe new space, can be coupled to the first two PCs and remappedto hand original (m)-DOFs using the PCs matrix obtained from ex-perimental data. This is expressed in Eq. (3):

→

PC1→

PC2 . . .→

PCm

In1In20. . .

=

Out1Out2Out3. . .Outm

. (3)

In Eq. (2), output vector consists of the desired (m)-DOFs of thehand. The remaining components of the input vector, which areto be multiplied by the last PCs, are set to zero, in order to ne-glect the less significant PCs contribution. This strategy could beexploited with a myoelectric hand prosthesis, where only few sig-nals are available for control, but dexterity is desirable. By usinginverse PCA algorithm, [48], all DOFs of a dexterous robotic handmaybe controlled in synergy bymeans of a simple two-signals con-trol interface, e.g. two independent EMG channels tapped from theresidual limb.

Trials have shown that two independent input signals can besuccessfully used to control the posture of a real robotic handand that correct grasps (in terms of involved fingers, stabilityand posture) may be achieved. Their research work demonstratedan effectiveness of a bio-inspired system successfully conjugatingthe advantages of an underactuated, anthropomorphic handwith a PCA-based control strategy. This will open up promisingpossibilities for a development of an intuitively controllable handprosthesis.

5.4. Biomimetic grasp planning for cortical control of a robotic hand:synthesizing stable grasps

In [49], Ciocarlie M. et al., and in their research frameworkmanuscript, they outlined a grasp planning system designed toaugment the cortical control of a prosthetic arm and hand. A keyaspect of this task is a presence of an on-line user input. Such anarrangement is shown Fig. 30. The line user input is ultimatelybe obtained by identifying and extracting the relevant signalsfrom brain activity. The grasping system can combine partial ornoisy user input and autonomous planning to enable the robot toperform stable grasping tasks. Ciocarlie M. et al. [49] have usedprincipal component analysis applied to the observed kinematicsof physiologic grasping to reduce the dimensionality of handposture space and simplify the planning task for on-line use.


Fig. 30. Planning for cortical control of a robotic hand, [49]. (Left): Experimental setup and object set used for recording primate grasps. (Right): Examples of interactivegrasp planning using input provided by an operator.

Fig. 31. Planning for cortical control of a robotic hand, [49]. (Left): Grasp plannedwithout using reference pose. (Right): Difference between planned grasps and input grasps,shown as normalized distance between the variables that define the grasp: solid line shows the difference in the amplitude of the first eigengrasp, while dashed line showsthe difference in wrist orientation.

Theplanner accepts control input in this reduced-dimensionalityspace, and uses it as a seed for a (Hand Posture Optimization Algo-rithm) based on simulated annealing. Two applications of such analgorithm were presented, using data collected from both primateand human subjects during grasping, to demonstrate its ability tosynthesize stable grasps using partial control input in real or near-real time. This is also clearly shown in Fig. 30. Difference betweenplanned grasps and input grasps, shownas normalized distance be-tween the variables that define the grasp: solid line shows the dif-ference in the amplitude of the first eigengrasp, while dashed lineshows the difference in wrist orientation. A value of (−0.2) wasused as a starting point for the Input Confidence axes to representthe case where planning was carried out without any kind of in-put, [49] (as can be seen in Fig. 31).

6. Conclusion and future developments

Recently, significant advances have been made in robotics, ar-tificial intelligence and other cognitive related fields, allowing tomake much sophisticated biomimetic robotics systems. Buildingbio-inspired dexterous robotics hands, that are actuated by feasibleartificial muscles, and controlled by high level cognitive artificialintelligence related techniques, would enable engineering reality,that used to be considered far from reality. Within this scope, theintention of this manuscript, is to look into and to survey a num-ber of research efforts towards building biomimetically inspireddexterous robotics hands. This depth study has indicated that,there are tremendous number of efforts towards building dexter-ous robotics multi-fingered handwith biomimetic based ideas and

initiatives. In addition, it was shown that, current research direc-tions are even moving towards muscles type hand fingers. Thismeans moving totally from the current concept of motor drivenjoints movements and towards much delicate and noiseless actua-tors, such as muscles. Building robots that are actuated and drivenby artificial muscles and controlled by a sort of intelligence, wouldcreate a novel and creative reality with enormous potentials to theindustrial and domestic employment of robotics technology thesedays. The survey has also indicated that, there are substantial ef-forts and potentials, related to articulated hands. All such effortswere directed towards improving robot hands dexterity and per-formance. This involves branches including, HAND DESIGN ANDARTICULATION, MECHANICS OF MOVEMENT, HAND DEXTERITY,TACTILE SENSING. In addition, at the lower end of the hierarchywe look at (skillfully fingertip movement), and AI related manipu-lation at the higher end.First: bio-inspired dexterous robotics:

Since the early eighties, building bio-inspired robotics handshas been a focus for quite large number of researchers world wide.The study has also shown that, development and technologies thatpermit developing biologically inspired systemand, in particularly,humanlike robots are increasingly emerging recently. There area number of successful stories in that regards. As it was claimedearlier, an effective and smoothness of fingertips movementsare very valued, once considering multi-fingered robotics handsand robotics dexterous manipulation. Although, literature hasshown successful hand design stories, it can be easily claimedthat, previous, and to a certain extent, CURRENT METHODOLOGY


of building dexterous robot hands are hindering high level ofdexterous and manipulation skills. Before a biomimetic survey ofdexterousmanipulation systemcanbedocumentedhere, obstaclesof topological dexterous manipulation has to be taken. Thoughdexterous manipulation demanding issues are well understood,and well documented within the robotics literature, there iscurrently no robotics hands system that autonomously have fulldexterous manipulation ability for smaller scale environment.Some other opinions have indicated that, reliable bio-orienteddexterous robotics hands could be the challenge of the nearfuture. Once this is achieved, a number of interesting questionsconcerning reliable bio-oriented dexterous robotics hands can beaddressed; for instance, how new technologies can be integratedto manufacture robotics hands with some desired behaviors. Thiswill include (hand kinematics and configurations), (hand dynamicsandmechanics of movement), (hand control), and (AI related handbehavior).Second: bio-inspired dexterous robotics hands oriented topology andhuman hand like designs:

Within this scope of area, building articulated hands hasalso continued for a while, since some early attempts of theBelgrade/USC [1], Utah [5], JPL [6], and CybHand [7], in additionto other attempts from time to time. In general, while surveyingsuch techniques and trends, it was very obvious that attempts byByoung K. et al. [9,10], Lee S. et al., Pollard N. and Richards G., [11],ZURICH–TOKYO HAND by Pfeifer R. et al., [12], and the (SquseCompany), as in [13], are targeting a more functional hand designslike human hands. This is due to the identical reasons for sucha direction, as human hands are more articulated and dexterousfor achieving complicated robotics defined tasks. This survey hasindicated that, there are quite large number of attempts to acquirebiologically inspired hands. Such inspirations were directedtowards a number of topics related hand design, hand tactilesensing, hand actuation and hand configuration and topology.Very rear research was directed towards the employment of brainand cognitive sciences towards robotics hand control. Althoughstill numerous biological examples remain to be explored, thenumber of well-studied biological mechanisms suitable for robotimplementation is relatively small. Consequently, the current stateof the art development cannot go on infinitely. It is very clearthat future biomimetic systems will have to be designed in closecollaboration with on-going biological research. This will permittechnical results to straightly influence the course of empiricalresearch, thus providing an innovative, synthetic approach tobiology. It will be very practical to have a robotics hand designinspired by the compliance characteristics, as it was advised byByoung K. et, al. [9]. However, building a robust and accuratecontroller for a typical design, is a complicated task.Third: bio-inspired soft actuation materials:

Following researches efforts by Price A. et al. [14], Bundhoo V.et al. [15], Cohen G. Y. [16–19], and [20], the Shadow Dexter-ous Hand design [21], and the introduction to (Ionic Polymer-MetalComposites (IPMC)), as given by the study of Shahinpooret al. [22], indicate with very clear and obvious reasons, thatnew technologies of actuation are coming over the nearest future.Throughout the review, it can be concluded that; EAP, Electro-Active Polymers Emergence is a new direction for robotics actu-ation. Hence, muscular type hand actuation is not a far target.Electro-Active polymers have emergedwith great promise and en-abled the improvement of uniquemechanisms that are biologicallyinspired. The development of an effective infrastructure for thisfield is also critical to the commercial availability of robust EAP ac-tuators and the emergence of practical applications. For the case ofbuilding reliable bio-oriented dexterous robotics hands, challengesare enormous, but the recent trend of international cooperation,the greater visibility of the field and the surge in funding of related

research are offering great hope for the future of making new ex-citing new materials that will help in design and implementationof bio-oriented robotics hands. It was found that technologies thatpermit developing biologically inspired system are increasinglyemerging. This includes robots that carry out suchmovement tech-niques as walking, hopping, swimming, diving, crawling, grasping,and manipulation. Bundhoo V. et al. [15] workout an integrationof (compliant tendon cables) and one-way shape memory alloy(SMA) wires in an agonist–antagonist artificial muscle pair config-uration for the required flexion/extension or abduction/adductionof the finger joints. Hence, the proposed biomimetic actuation andfinger system has a number of features. (i) anthropomorphicallyaccurate size and appearance; (ii) it is also kinematically accuratejoint motion, (iii) compliant and tendon-driven muscle-like actua-tion, and (iv) biomimetic sensory feedback.Fourth: bio-inspired tactile sensing:

There have been excellent efforts in terms of building artic-ulated robot hands with bio-inspired tactile sensing. Within thisstudywe have looked into ten new trends and efforts for achievingsuch a directions. This includes, Takahashi T. et al. [23], Ulmen, J.and Cutkosky, M. [24], Cutkosky, M. [26], Ravinder D. [29], Cas-sidy A., [30] and Ekanayake V., [31], Wettels N. et al., Wettels N.et al. in [32,33], Petroff N., [34] Edinc B. et al., [34], and Ascari L.et al., [35]. However, the most attractive effort was the one intro-duced by Ulmen, J. and Cutkosky, M. [31]. They have introduceda more realistic force sensing, with electronics processing power.Through such area sensor, a robot handwas able to detect an eventof slippagewhile grasping. The attempt and experimental setup byTakahashi T. et al. [23] shows a good model for integrating tactile,force, and position control. Ulmen, J. and Cutkosky, M. [24] havereported in their paper [24], that with its low-cost and high scala-bility, the presented sensor designmakeswhole body sensor arraysfeasible. Also, the tactile sensing array was presented with appli-cation as a whole body artificial skin covering.

In [25], they employed results of a study, already presentedin [24], with various sensors, hence processed to determine tactileand touching events, such as MAKING/BREAKING contact or SLIPbetween the hand and grasped objects, as well as CONTACTLOCATION and STATIC PRESSURE. Such grasping events, and thecontinuous contact information can then be used to control morecomplexmanipulations. In a general sense, the development of thisclass of tactile sensing and its use for a robotics hand, as in [24,25], gives a good degree of indication, in which direction advancedtactile sensing is heading now. In their valuable surveying researchframe work, Cutkosky M. and Howe R., [27], indicated that,in the near future, it will be increasingly possible to fabricatedense arrays of transducers in-situ on contoured surfaces, usingmaterial deposition and laser machining techniques. In this way,robots may finally start to approach the tactile sensitivity andresponsiveness of the simplest of animals. Another successfuleffort for building a bio-inspired tactile sensing was introduced byAscari L. et al. [35]. They have reported in their research paper a(bio-inspired approach), the working principle of the architecturebases on the cellular nonlinear/neural network paradigm, whileusing both hand shape and spatial–temporal features obtainedfrom an array of micro-fabricated force sensors, in order to controlthe sensory-motor coordination of the robotic system. The novelidea behind the presented design of Cassidy A. and EkanayakeV, [30], is that it is, an intelligent tactile sensor array, that it canaccurately measure the (magnitude) and (direction) of incidentforce, aswell as determine if a slip has occurred. They also indicatedthat, a final research goal, is to implement and fabricate the tactilesensor array as a custom integrated circuit. For the presentedworkofWettels N. et al. in [32], it was found that, the sensor is compliantto support grip, and its design is such that simple algorithms canbe implemented in real-time to calculate normal and tangential


forces regardless of point of contact. Nonholonomic constraintsarise during contact, requiring the fingers to only roll relative to theobjectwithout slippage. This issuewas discussed by Petroff N. [33].However, the algorithm gives rise to new Vector Fields called{Lie Brackets} that allow the fingers to be reconfigured withoutreleasing the object, effectively increasing the workspace of themanipulation system.

Fifth: bionic and prosthetic hands:The study has also looked into a current demanding issue,

that has also new research directions, and has almost a similararea of use as for robotics use. This is related to bionic andprosthetic hands. A point that we need to consider is that, thereis a technological overlap between dexterous robotics and bionicand prosthetic hands. This is due to the similar area of demand anduse. This area of research is also receiving a considerable attention.Even some products are already put as commercial products. Forsuch trends, the survey has looked into four cases for bionic andprosthetic hands.We looked into four research efforts, this includethe I-Limb Ultra Prosthetic HandTM [45], Craelius W. et al. [46],Matrone G. et al. [47], Ciocarlie M. et al., [49]. In reality, theiLIMB Hand has been introduced lately as an example of the mostadvanced bionic hand. It presents also an example of how farcommercial artificial hands can move. In terms of recent researchoutcomes, there have been also a number of efforts to use andintegrate EMG body signals for controlling finger movements. Thiscan be seen clearly form the research outcome of Craelius W.et al. [46]. The overall design goal was to use natural tendonmovements in the forearm to actuate virtual finger movement.

Finally, in a summary, it is essential to mention that, this surveyhave been focusing on efforts related to robotics articulatedhands. The study has been more focused on design issues, andfew number of developments that have been taking place lately.Through this study, the survey has been kept a distance of handmathematics andmodels (this include articulated hands dynamics,kinematics, grasping forces and distributions, and configurationoptimizations), the mathematical issues. The intention for sucha tendency, is that, such mathematical backgrounds, have beenalready reported and appeared in robotics literature for a quite awhile. It is worth to mention that, Bicchi A. [50], has already putforward a study about the modeling issues relating robotics handsand a grasping dynamics andmodeling, another literature can alsobe found in Murray R. et al. [51], in addition to a comprehensiveliterature found in [52,53]. However, with emerging new handdesigns, actuating mechanisms, and sensory feedbacks, suchgrasping models need to be revisited in terms of extracting newmodels. Not only updated hand models are needed, however,laying out much appropriate controllers algorithms to take care ofsuch new building materials, are essential emerging demands forarticulated hands. Throughout the study, it has been found thereare number of efforts in terms of mechanics and hand designs,tactical sensing, however, for hand soft actuation, it seems this areaof research is still far away from having a realistic muscular typefingers and hand movements.

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Dr. EbrahimMattar is an Associate Professor of IntelligentControl and Robotics at University of Bahrain. Mattar isalso a candidature for a Professor rank. He has receivedBSc. in Electrical Engineering (from University of Bahrainin 1986), doneMSc. in Electronics in 1989 (fromUniversityof Southampton, UK), and in 1994 he received Universityof Reading Ph.D. in Cybernetics and Robotics Control.Dr. Mattar has interests in Computational Intelligence,Robotics Control, Modeling and Control. This includesclustering with fuzzy, Neural Networks, EvolutionaryComputation, and their real applications in Robotics and

Control. Now he is working in Hinf optimal robotics control system, intelligentcontrollers forMulti-finger Robotics Hands, Hand Visual Servo system, active visiondexterous hands manipulation, and hybrid systems for uncertainty analysis forrobotics modeling. Mattar is a member of a number of professional societieslocally and internationally. Locally, he is a member of Bahrain Society of Engineers(BSE), board member of the Academic Society in Bahrain, board member of theTechnology Transfer Society. Internationally, Mattar is a member of the IEEE,member of IEEE Control Educational Society, IEEE Robotics and Automation Society(RAS), an active IET member, IET — Bahrain Network Honorary Chair, X-IETEMEA Regional Board Member, IET Knowledge Programme Advisory Member,and member of some control societies world-wide. Dr. Mattar has published anumber of journal and conferences articles in the area of dexterous robotic hands,Control, Optimal robotic hand forces, Neural multi-finger robot hand graspingand control, optimal fuzzy control, and Neuro-fuzzy systems, intelligent control.Dr. Mattar has been a responsible body for two major conferences in Bahrain,including the IET ICIS-2008 (International Conference on Intelligent Systems,December 2008), and a responsible body for three IET symposiums in Bahrain,including (New Directions in Automatic Control : Theories and Applications, April2010. Dr. Mattar has also organized large number of IET events in Bahrain since2006 till this moment. Dr. Mattar is also an ABET accreditation expert, as hasbeen leading a team for a positive full Electrical and Electronics EngineeringPrograms accreditation over the period from 2005–2010, for College of Engineeringat Bahrain University. Run up to 18 short Courses in the Area of EngineeringControl. These Short Courses have been serving different industries in BahrainElected Honorary Chair for the IEE and IET for two Sessions, Bahrain Local NetworkCommittee. Written and still writing (In Arabic) a number of newspaper articlesabout Higher Education, Research and Engineering History. Organizing Head forthe International Conference on (Millennium Dawn in Training and ContinuingEducation), 16–18th of April 2001. A responsible organizer for a Power Forum: (Trends Towards Power System Networks Enhancement), 26th February. 2008.A responsible organizer for a Communication Forum : Challenges and Trendsin Modern Communication), 7th May 2008. A responsible organizer for an IETInternational Conference on Intelligent Systems (ICIS-2008), 1–3rd December2008. A responsible organizer for an IET Control Symposium: (New Directionsin Automatic Control: Theories and Applications), 26th of April 2010. ChairingContinuing Engineering Education Program, University of Bahrain (1998–2002).Chairing Electrical and Electronics Engineering Department, University of Bahrain(2004–2009), also seconded from UOB as the Director General of the BahrainTraining Institute (2011–2012). Head of ABET Accreditation Committee (DAC) fortwo terms, (2005–2012). Awarded University of Bahrain, Deanship Best ResearchProject award in 2001. University of Bahrain, College of Engineering ResearchAward, 2002. University of Bahrain, College of Engineering best graduation ProjectAward (2001). (2001–2002) University of Bahrain, President Achievement Awards,(2002). University of Bahrain, College of Engineering best graduation Project Award,(2003). (2005–2006) University of Bahrain, College of Engineering best graduationProject Award. University of Bahrain, College of Engineering best graduation ProjectAward, (2006). Bahrain Society of Engineers Award for two successive terms 2007& 2008. A responsible organizer for an IET Robotics Forum, 21st of December 2011.A fully responsible organizer for an IET Control Symposium : (New Directions inAutomatic Control: Theories andApplications), 29th ofMay2012. Now I amheadingas the third time of duty with University of Bahrain.

http://www.ottobock.com/cps/rde/xchg/ob_com_en/hs.xsl/49809.html

http://www.medgadget.com/2011/09/touch-bionics-introduces-i-limb-ultra-prosthetic-hand



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A survey of bio-inspired robotics hands implementation: New directions in dexterous manipulation