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Abstract This paper describes about the ongoing projectaiming to the development of the new methodologies. Thispaper reports our research on service-oriented roboticscomputing and our design, implementation, and evaluation of Robot as a Service (RaaS) unit. To fully qualify the RaaS as acloud computing unit, we have kept our design to comply withthe common service standards, development platforms, andexecution infrastructure.A dream of humanoid robotresearchers is to develop a complete human -like (whateverthat means) artificial agent both in terms of body and brain. Wenow have seen an increasing number of humanoid robots.These, however, display only a limited number of cognitiveskills in terms of perception, learning and decision-making. In

this paper, we propose an intermediate approach for robot foruse in a shopping mall to provide information, offer routeguidance, and build rapport. For this purpose, we haveimplemented our RaaS on Windows and Linux operatingsystems running on Atom and Core 2 Duo architectures. RaaSsupports programming languages commonly used for service-oriented computing such as Java and C#. Special efforts havebeen made to support Microsoft Visual Programming Language(VPL) for graphic composition.An ongoing issue in humanrobot interaction (HRI) is how people and robots communicatewith one another. While there is considerable work in real-timehuman-robot communication,fairly little has been done inasynchronous realm.The developed robot system detects aperson with floor sensors to initiate interaction, identifies

individuals with radio-frequency identification (RFID)transmitter and receiver, gives shopping information whilechatting, and provides route guidance with deictic gestures.Therobotwas partially teleoperated to avoid the difficulty of speechrecognition as well as to furnish a new kind of knowledge thatonly humans can flexibly provide..

Categories and Subject DescriptorsI.2.9 [Robotics]: Operator InterfacesGeneral Terms: Experimentation, Human Factors, DesignKeywords: Human-Robot Interaction, Raas,Iaas,SaasInformation-providing, network robot system,

I. INTRODUCTION

THE HUMAN ROBOT began in Guadalajara, Jalisco,Mexico in 1973. Cloud computing is a new paradigm inwhich computing resources such as processing, memory, andstorage are not physically pres ent at the users location.Instead, a service provider owns and manages theseresources, and users access them via the Internet. Forexample, Amazon Web Services lets users store personaldata via its Simple Storage Service (S3) and perform

This work was supported in part by Dr. MG.BASKARAN the chairman of Bhajarang engineering college .(2011),and our beloved E.C.E H.O.DMr.ANNAMALAI and staff faculties of Bhajarang Engg College

computations on stored data using the Elastic ComputeCloud (EC2). This type of computing provides manyadvantages for businesses including low initial capitalinvestment, shorter start-up time for new services, lowermaintenance and operation costs, higher utilization throughvirtualization, and easier disaster recovery that make cloudcomputing an attractive option. Reports suggest that there areseveral benefits in shifting computing from the desktop to thecloud. What about cloud computing for mobile users? Theprimary constraints for mobile computing are limited energyand wireless bandwidth. Cloud computing can provideenergy savings as a service to mobile users, though it also

poses some unique challenges.Emb racing the cloud could make robots lighter, cheaper,

and smarter, he said in his talk, which created much buzzamong attendees ,psychiatrist who introduced us to theconcepts of the human robot. Human robots are people theyare expected to interact with people and support dailyactivities. In particular, humanoid robots are already beingused to provide help with physical activities [1]. While thedomain of science fiction, robots are now established in thedomestic setting [5]. Commercial service robots, such as theiRobot Roomba, often perform tasks in households [5], [9].Because most of todays tasks are simple ones, such robotsare predominantly autonomous. However, we anticipate thatfuture robots will be able to perform a variety of different,more complex tasks to support families in their homes .

II. CLOUD ROBOTICS:

For us humans, with our non-upgradeable, offline meatbrains, the possibility of acquiring new skills by connectingour heads to a computer network is still science fiction. Notso for robots.For instance, previous studies have revealedthat robots can be used as museum guides [8], [9] in cityexploration [10], as receptionists who assist visitors [11], aspeer tutors in schools [12], in mental healthcare for elderly

people [13], in autism therapy [14], [15], and in childcare[16] reports our challenges in applying a robotic system to aninformation-providing task in the daily environment of ashopping mall. The difficulties included sensing the humanbehaviors and conversation in a noisy daily environment,theunexpected,needs.of.various.information,duringconversation. Our approach used a network-robot system tosupplement robots that rely on cloud-computinginfrastructure to access vast amounts of processing powerand data. This approach, which some are calling "cloudrobotics," would allow robots to offload compute-intensive

CLOUD ROBOTICS FOR CLOUD COMPUTING BASED INTERACTION

K 1.Thirumurugan, R 2.santhoshkumar N 3. Ramachandran Dr 4.S.Neduncheliyan.

Bhajarang engg college Arunai engg college Arunai engg college Bhajarang engg college

Thiruvallur-India, Thirunanamalai-India Thirunanamalai-India Thiruvallur-India

[email protected] [email protected] ramngr202 [email protected] [email protected]

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tasks like image processing and voice recognition and evendownload new skills instantly, Matrix-style.

I.HUMAN-ROBOT INTERACTION

Human-computer interaction (HCI), as a field, has madegreat strides toward understanding and improving ourinteractions with computer-based technologies. From theearly explorations of direct interaction with computers, wehave reached the point understanding and improving ourinteractions with computer-based technologies. From theearly explorations of direct interaction with computers, wehave reached the point Semiotics studies the interactionsamong human These are often characterized by the loosespecification of objectives for instance as when a sentenceexpresses an intention of motion instead of a specific pathwhere usability, usefulness, and an appreciation of technologys social impact, including its risks, are widelyaccepted goals in computing. HCI researchers, designers,and usability engineers work in a variety of settings on many

kinds of technologies. Recent proceedings of the CHIconference give evidence of this diversity. would allowrobots to offload compute-intensive tasks like imageprocessing and voice recognition and even download newskills instantly, Matrix-style. Imagine a robot that finds anobject that its never seen or used before say, a plastic cup.The robot could simply send an image of the cup to the cloudand receive back the objects name, a 3 -D model Topicsinclude not only the office systems where HCI work began,but also tiny mobile devices, web and Internet services,games, and large networked systems. This special issueintroduces a rapidly emerging technology and new focus forHCI autonomous robots and the human-robot interactionsrequired by these robots. For conventional robots, everytask moving a foot, grasping an object, recognizing aface requires a significant amount of processing andpreprogrammed information. As a result, sophisticatedsystems like humanoid robots need to carry powerfulcomputers and large batteries to power them.cloud-enabledrobots could offload CPU-heavy tasks to remote servers,relying on smaller and less power-hungry onboardcomputers. Even more promising, the robots could turn tocloud-based services to expand their capabilitiesCloud robotics could make that possible by expanding a

robots knowledge beyond its physical body.Coupling

robotics and distributed computing could bring about bigchanges in robot autonomy

LITRATURE SURVEY:

Aylett,Barnes,1998),(Huntsberger et al., 2003),(Albus,1987),(Kortenkamp et al. If the humans are assumed to haveenough knowledge on the robots and the environment,imperative computer languages can be used for HRI, easilyleading to complex communication schemes. Otherwise,declarative, context dependent, languages, like Haskell,(Peterson and FROB, (Hager andPeterson,1999), have been

proposed to simulate robot systems and also as a mean tointeract with them. BOBJ was used in (Goguen, 2003) toillustrate examples on that convey information on theirintentions to the outside environment. These behaviorsrepresent a form of implicit communication between agentssuchas the robot following a human without having beenhuman-computer interfacing. RoboML,(Makatchevand Tso,2000), supported on XML is an example Capturing some of

these features.

Figure 1: A comparison of (a)traditional robotic controlsystem and (b)theService-Oriented Architecture

II.S YSTEM OVERVIEW

Figure 2: A guiding approach is as shown in the above fig

Avoid wasting energy. Whole systems or individualcomponents may enter standby or sleep modes to savepower. Execute programs slowly. When a processors clock speeddoubles, the power consumption nearly octuples. If the clock speed is reduced by half, the execution time doubles, butonly one quarter of the energy is consumed. Eliminate computation all together. The mobile systemdoes not perform the computation; instead, computation isperformed somewhere else, thereby extending the mobilesystems battery lifetime.We focus on the last approach for energy conservation.

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fig Shows The Layout Map Of The Robovies

III. T HE MAIN ELEMENTS OF THE ROBOT SYSTEM

The main robots and agents of the robot System are nowdescribed in greater detail. We will describe them in theorder in which a patient is likely to encounter them.

A. Robot Registration Assistant

The Robot Registration Assistant would be at theregistration desk and would be the first robot encountered bythe patient. This robot will have a humanoid form and willneed a high degree of ability to interact with the patient. Itmust be able to recognize humans and track them in its

environment. Additionally, it must be able to engage inbasic conversation with the registering patients. Of course, itmust also be able to interact with the other agents in thesystem.

This robot will gather basic patient data such as name,address, telephone numbers, insurance information, etc. Itwill also start gathering some diagnostic data by asking suchquestions as What is the chief complaint?, Where is thepain? and What is the level of pain?. Visual AnalogScores may be used to assess pain levels, shortness of breath,etc. The methods of interaction used for gathering this datamay include voice dialog and touch sensitive screens as may

be encountered in a smart kiosk. This data is entered into thepatients file, and then the patient is directed to the RobotTriage Nurse Assistant for gathering other diagnostic data.

B. Robot As A Assistant

This robot is likely to have a specialized form designedspecifically for taking measurements. A likely form is that of a chair instrumented with the necessary sensors. In general,the high-level interaction skills of this robot are less complexthan the others, since its duties are more specificallyprescribed. On the other hand, it will require a higher levelof motor skills since it will be responsible for takingmeasurements directly from the patients. After this data isgathered, the patient is sent back to the waiting room wherehe/she will be monitored while waiting for treatment.

C. Robot Monitoring Assistant

After reviewing all the data collected, the RobotMonitoring Assistant selects an appropriate time interval forchecking up on the patient in the waiting room. This robotwill periodically check to see if the patient is still in the

waiting room, if they are conscious, and possibly take simplemeasurements such as blood pressure and pulse rate.Additionally, it may inquire about the level of pain. There issome flexibility in the form of this robot. It is likely to be amobile robot and may or may not have humanoidcharacteristics. It will require a substantial level of cognitiveskill in order to interpret and respond to a wide variety of events and interactions in the waiting room.

D. Supervisor

The Supervisor will act as the central manager of all therobots, as well as providing an interface to hospital personneland databases, except for the doctors and nurses that interact

directly with the patients. They, of course, will still have thedirect interfaces that they usually use. Additionally, thereare likely to be sensors, such as cameras, monitoring thewaiting room and possibly the treatment rooms. Thesewould enable the Supervisor to check for important eventsincluding whether a patient has fallen to the floor or whethera patient is still conscious. Finally, the Supervisor maycalculate possible diagnoses and suggest early testing orother non-physician care.

E. Communication:

Communication has many facets. Direct human-robotcommunication is possibly the most obvious issue.

Modalities include: speech, vision, gesture, and tele-operation, though there may be other forms. Mediatedhuman-robot communication is another topic. This arisesfrom virtual environments, graphical user interfaces, and canbe enacted by collaborative software agents. The physicalinteraction and interfaces impact communication. Thesemethods include physical interaction between robots andhumans, mixed initiative interactions between humans androbots, and dialog-based interaction. There are many aspects

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of interaction and interfaces which need to be explored.Inferring intent of an agent was noted as being critical.

F. modelling:

Modeling issues spanned traditional concerns (cognitive,task and environment modeling) to more HRI specificconcerns. Cognitive modeling of human reasoning, behavior,

intention and action is needed for imitation (i.e., the robotlearns how to behave from the human) and for collaboration(i.e., the robot understands what the human is doing withinthe context of the task). Task and environment modeling areneeded as a basis for performance.

IV. A RCHITECTURAL CONCERNS

A. Requirements for a Robot in a Partially Structured Environment

Humans process sensory data, and select and beginexecuting a response five to ten times a second [18].Humanoid robots, operating in human-like environmentsshould also be able to process sensory data and chooseactions at a similar rate of 5-10 Hz. Humans dealcontinually with tremendous amounts of sensory data, muchof it irrelevant, by employing their attention mechanisms as afilter. A humanoid robot living in a typical partiallystructured environment should also filter large amounts of sensory data using an attention mechanism. This implies thatthe robot must be capable of attentional learning, that is, of learning what to pay attention to. Such learning would seemto require both top-down and bottom-up processing, as well

as the self-organization of concepts. The latter will alsorequire self-derived representation, that is, perceptuallearning. All this entails considerable bottom-up modifyingof representations and organizing, combined with top-downanalysis of performance.

If a cognitive humanoid robot has humans or databasesreadily available, say for example, via a wireless internetconnection, it might not have to be widely knowledgeable,being able to ask about what it doesnt know. That, of course, requires that it be smart enough to know when itdoesnt know [19]. In order for a cognitive humanoid robotto rely on humans or databases for knowledge, it must have

enough metacognitive ability to recognize its lack of knowledge. A cognitive humanoid robot operating well in ahuman-like environment had best be controlled by acognitive architecture capable of perceptual and attentionallearning, as well as of higher-level cognitive processes suchas metacognition [20] [21].

B. Establish dynamic sensory-behavior linkages

As the need for cognitive humanoid robots to becomeuseful partners in our society increases, it is important tolook beyond engineering-based control and learningapproaches. For example, humans have the capacity toreceive and process enormous amount of sensoryinformation from the environment, integrating complexsensory-motor associations as early as two years old [22][23]. Most goal-oriented robots currently perform only thoseor similar behavioral tasks they were intended for. Very littleadaptability in behavior generation is exhibited when animportant environmental event occurs. What is needed hereis an alternative paradigm for behavioral task learning andexecution. Specifically, we see cognitive flexibility and

adaptability in decision making in our brain as a desirabledesign goal for the next generation of cognitive robots. Forexample, human decision making is strongly influenced byour internal states such as emotions. A change in internalstate results in changes in our perception of which goals aremore important. This type of decision making leads to moreacceptable solutions rather than precise engineeringsolutions.

C.architecture, perception, attention, and situationalawareness

As pointed out earlier, each of the robot assistants, as wellas the software agent supervisor, will require a cognitivecontrol architecture. Perception systems will play a criticalrole for the performance of the motor actions in each of therobots and in the supervisor. Each robot may encountermany percepts at any given moment, and many of them maybe distractors for the current task. The limited capacityproperty of an attention system provides focus for the robotsto search for appropriate actions in order to accomplish thegiven tasks. A significant role of the attention system is thedetermination of which chunks 1 of information should beactively retained, and which may be safely discarded, for thecurrent critical task success. Furthermore, the emergencydepartment domain will require of each robot assistant, and

of the supervisor, considerable situational awareness, that is the perception of elements in the environment within avolume or time and space, the comprehension of theirmean ing, and projection of their states in the near future.[29] Situation awareness by triage robot assistants in anemergency department setting includes being aware of unexpected events and of the unpredictable behavior of

patients. For these responsibilities, and more, we model.

1 In this context, the term "chunks" is used to refer to the memoryitems that are uti lized by the working memory.

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Fig 4 Shows The Interaction Humanoid Robot With Robot

Figure 5: A communication Robovies over view of systemArchitecture

III. HARDWARE AND SYSTEM DESIGN

To prove the concepts, we have implemented a prototype of the RaaS shown in Figure 2. To make the RaaS moreadaptable, we have made the following decisions in theimplementation:

Fig A RaaS unit

1.Hardware: Generic Intel processor and mother board areused. We have test the RaaS on Core 2 Due and Atomprocessors Robotics and service-oriented robotics computingstart to joint this new paradigm in the past five years and arenow ready to participate in large scale. This paper reportsour research on service-oriented robotics computing and ourdesign, implementation, and evaluation of Robot as a Service(RaaS) unit. To fully qualify the RaaS as a cloud computing

Operating systems : We have implemented an Windows XPversion and a Linux version.

Programming languages : We have used C# and Java toprogram the services and the applications. We alsoimplemented a service that interfaces Visual ProgrammingLanguage (VPL) applications to Intel platform.

1.A RaaS cloud unit is a service provider: Each unit hosts arepository of preloaded services. A developer or a client can

deploy new services into or remove service from a robot.The services can be used by this robot can also be sharedwith other robots.

2. A RaaS cloud contains a set of applications deployed: Adeveloper or client can compose a new application(functionality) based on the services available in the unit andoutside the unit.

3. A RaaS unit is a service broker: a client can look up theservices and applications available in the unit s directory. Aclient can search and discover the applications and servicesdeployed on the robot by browsing the directory. Theservices and applications can be organized in a hierarchy of classes to facilitate the discovery. The SOC software in RaaSwill communicate with the drivers and other operatingsystem components, which further communicate with thedevices and other hardware components. The RaaS units candirectly communicate with each other through Wi-Fi, if thewireless infrastructure is available or through Bluetooth, if two units are close to each other. The communicationbetween RaaS and other services in the cloud are throughstandard service interface WSDL. The communicationprotocol supporting the invocations is SOAP or RESTprotocol.

we have kept our design to comply with the common servicestandards, development platforms, and executioninfrastructure. We also keep the source code open and allowthe community to configure the RaaS following the Web 2.0principles of participation. Developers can add, remove, andmodify the RaaS of their own. For this purpose, we haveimplemented our RaaS on Windows and Linux operatingsystems running on Atom and Core 2 Duo architectures.

4.RaaS supports programming languages commonly used forservice-oriented computing such as Java and C# . As in aWizard of Oz (WOZ) method, speech recognition isconducted by a human operator. This inform-ation is sent to

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a behavior selector that chooses an interactive behaviorbased on preimplement ed rules called episode rules , whichare the history of previous dialogues with this person andher/his personal information .Our architecture is behavior-based, where low-level sub-modules can be restr-icted bymodules in higher layers

1) Position Estimation: We used external sensors fordetecting and tracking peoples posit ions around the robot,since we need such highly accurate detection to robustlyoperate the robot in crowded environments. We chose floorsensors, because they are the most robust device in terms of stabilit y to detect a persons presence, and their area can alsobe used to indicate the conversational distance of the robot,where people can interact with it. unveiled their Androidpowered robot software and a small mobile robot dubbedthe cellbotThe software allows an Android phone to controlrobots based on Lego Mindstorms and other platformsItcould apply to any kind of robot, large or small, humanoid ornot. Eventually, some of these robots could become morestandardized, or de facto standards, and sharing applications

would be easier.

.

Figure 6 Shows The Hard Ware And Software Design2) Person Identification With RFID Tag:Various techniques for person identification exist, includingFig. 7. RFID tag and reader.recognize faces, active-typeRFIDs, and passive-type RFIDs. For person identification,we employed a passive-type RFID tag because of their 100%accuracy in person identification .Such accuracy is crucial,since the misidentification of a person causes embarrassinginteraction in human communication. One downside is that a

system based on passive-type RFID tags requires intentionaluser contact with an RFID reader; since passive-type RFIDsare already widely adopted for train ticket sin Japan, peopleare accustomed to using them. We do not consider thisproblematic.The left side of Fig. 7 shows a passive-typeRFID tag (Texas Instruments Incorporated, RI-TRP-WRHP)embedded in a cellular phone strap that uses a frequencyof134.2 kHz. The accessoryis 4 cm high. The RFID tags

reader is attached to the robots chest. Since a passive -typeRFID system requires a contact distance for reading, userswere instructed to place the tag on the tag reader foridentification and to interact with the robotWe provided thisRFID tag to mall customers,who registered for thefieldtrial.Once an RFID tag is read by the reader, the systemassociates the persons ID with the perso n detected by thefloor sensor .When multiple persons are tracked withthefloor sensors, it a ssociates the IDto the nearest person.Once the ID is associated,it keeps tracking the ID until theperson with the ID leaves thearea on the floor sensors.Thelocation of the person with the ID is repeatedly used during

interaction. The robot orients the body direction andmaintains eye contact; the interaction is concluded when theperson leave.Each situated module has a unique identifiercalled a Module ID. refersto the execution history and the result value of the situatedmodules. . . . means the referring rule of the previo uslyexecuted sequence of situated modules (see Table V, item 1). denotes a selec tive group (OR) of theexecuted situated modules , and ( . . . ) means a block thatconsists of a situated module, a sequence of situatedmodules,or a selective group of situated modules3). Similar to regular expressions, we can describe therepetitionof the block as ( . . . {n, m} , where n gives the minimumnumber of times that match the block, and m gives themaximum (see Table V, item 3). We can specify the negationof the whole episode rule with an exclamation mark !.For example, ! . . . NextModuleID (see TableV,item 4) means the module of NextModuleID will not beexecuted when the episode rule matches the current situationspecified by . . . . The negation of a ModuleID or a result value is written with a caret: (see Table V,item 5).

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4) Using dialog history: The robot has a mechanismfor adjusting its interactive behaviors to eachindividual based on its dialog history. For example, therobot asks whether the persolikes icecream on day one;when the person revisits the roboton day two, the robotstarts a con versation: Last time, you saidthat you likedice cream. Well, I asked about ice cream in thisshopping mall, and I found . . .

Fig7: shows the face tracking system

Face-Tracking System:We developed a face-tracking algorithm for a

communication robot that integrates information from bothfoveal and omnidirectional visions and actively controlstherobots head orientation [30]. a robot would send images of what it is seeing to the cloud, receiving in return detailedinformation about the environment and objects in it. Using

the cloud, a robot could improve capabilities such as speechrecognition, language translation, path planning, and 3DmappingNow cloud robotics seeks to push that idea to thenext level, exploiting the cheap computing power andubiquitous Net connectivity available today.

However, in a real environment,false-positive faces werefrequently detected, whichlargely hindered the performance.

Thus, in addition to this basic mechanism, we usedinformationfrom person tracking and identification. Thesearch area ofa persons face is limited to the ar ea, wherepeople are detectedby floor sensors. When the person isidentified by the RFID tags,the system retrieves the personsheight information, which isprestored to vertically limitthesearch range.With these combinations,the robot wasusually able to orient its gazing directionto a users face.

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Fig 9: shows the face tracking system. And diagnosis in facetrackin

V.C HALLENGES ON PERFORMENCE

Operating a cognitive system in the complex dynamic

environment of an ED poses many challenges. Somechallenges we have identified include the following. A largenumber of experiments have been performed and data onperformance and power consumption are collected fordifferent processors (Core 2 and Atom) and at different clock rates. This section will present a sample set of data and theinterpretation of the data. Experiment setting: Experimentswere conducted on Intel Core 2 Duo and Intel Atom N270processors by running a specific number of threads inparallel. Each thread represents an instance of a service. Theexperiments have been conducted on physical services thatread sensors and control actuators. For Intel Atom N270, theexperiment was conducted for 1.6 GHz only, as the Atomwas running in Windows XP, which does not support theadjustment of clock rate. The data collected are shown inFigure 12. The processor usage data collected in theexperiments give us a good idea how many services,modeled by the number of threads in the diagrams, a RaaScan host, and how many requests, modeled by the sleeptimes/request intervals in the diagrams, a RaaS can handle.If a RaaS is running significantly lower number of servicesthan its full capacity, the system should adjust its clock rateto a lower level to achieve desired performance whilereducing processor heating and increasing powerconsumption. Processor temperature and power consumption

rise exponentially to the clock rate increase. Similarly, onemay make a choice between a multiprocessor and a singleprocessor system as per the estimated load of the system.We also collected data for other type of Intel processors, aswell as the on power consumption for the processors. Thedata will be presented with the further experiments in thesubsequent

publications.

CONCLUSIONS

OUR ANALYSIS SUGGESTS THAT CLOUD COMPUTING CANPOTENTIALLY SAVE ENERGY FOR CLOUD BASED SYSTEMS USERS .HOWEVER , NOT ALL APPLICATIONS ARE ENERGY EFFICIENT WHENM IGRATED TO THE CLOUD . THE SERVICES SHOULD CONSIDER THEENERGY OVERHEAD FOR PRIVACY , SECURITY , RELIABILITY , ANDDATA COMMUNICATION BEFORE OFFLOADING .THIS PAPER DEFINEDTHE CONCEPT OF ROBOT AS A S ERVICE (RAAS), P RESENTED THEDESIGN AND IMPLEMENTATION OF A R AAS PROTOTYPE . THEFEATURES OF THE RAAS DESIGN INCLUDE (1) A LL-IN-ONE DESIGN

OF SERVICE PROVIDER , SERVICE BROKER , AND SERVICE CLIENT INRAAS ; (2) G ENERIC HARDWARE BASED INTEL ARCHITECTURE ANDGENERIC USB AND COMMON SERIAL PORT DEVICES ; (3) G RAPHICCOMPOSITION BASED ON ROBOTICS DEVELOPER STUDIO AND VPLLANGUAGE ; (4) M ULTIPLE DESIGNS HAVE BEEN IMPLEMENTED ONMULTIPLE PLATFORMS , INCLUDING JAVA ON LINUX AND C# A ND VPLON WINDOWS . EXTENSIVE EXPERIMENTS AND EVALUATIONS HAVEBEEN PERFORMED AND THE RESULTS SHOW THE FLEXIBILITY OF THEDESIGN THAT CAN BE EASILY PORTED TO DIFFERENT SYSTEMS . THEEXPERIMENTS ALSO SHOW THE EFFECTIVENESS OF THE SOFTWAREAND HARDWARE SYSTEM SUPPORTING THE COMPLEX RAAS UNIT THESTUDY PROVIDED FINDINGS COVERING A S ERIES OF TOPICS FROMCLOUD COMPUTING DESIGN CONSIDERATION TO USER FEEDBACK . ITGAVE AN EXAMPLE OF SOCIETAL ROLES OF A C OMMUNICATION

ACKNOWLEDGMENT

The authors would like to thank our beloved CHAIRMANDr.MG.BASKARAN Of Bhajarang Engineering College )Respected H.O.D MR.ANNAMALAI, And Mr.Kumaresan(senior Lecturer), Mr.Sentamilselvan (senior Lecturer),Ms.Heema and other Staff Faculties Of Our College.

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and Ecology of

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[2] Elders Living Independently in Their Homes. Human- ComputerInteraction, 19,

[3] RaaS maze demonstration, recorded in Dec. 2009:mms://asusrl.eas.asu.edu/srlab/Research/Robots/VPL/RobotMazeDec09.wmv

[4] [29] RaaS cooperation demonstration, Dec. 2008,mms://asusrl.eas.asu.edu/srlab/Research/Robots/PartyBots/PartyRotsASU.wmv

[5] [30] ASU, Smart home project service repository ,

http://smarthome.engineering.asu.edu/content/services[6] [31] J. D. Oliver, M. E. Garca-Acosta, J. Harris, F. Lajvardi, YinongChen, L. Gutierrez, Robotics Starter Kit Using Intel Architecture,Intel White Paper, Mar 2010,http://download.intel.com/d esign/intarch/papers/323505.pdf

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AZ 85287-8809, USA[9] *Department of Computer Science and Technology, Tsinghua

University, China[10] **Intel Corporation, Embedded Computing group, Chandler, Arizona,

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