Final report on autonomous mobile robot navigation

A SEMINAR REPORT ON

AUTONOMOUS MOBILE ROBOT NAVIGATION

DEPARTMENT OF PRODUCTION AND INDUSTRIAL ENGG

MBM ENGINEERING COLLEGE

(Submitted in the partial fulfillment of BE degree in PampI)

SUBMITTED BY GUIDED BY

CHANDERBHAN DR A K VERMA

BE Final Year ASSOCIATE PROFESSOR

ENo 0801325

Roll No 2216004

C ERTIFICATE

This is to certify that Mr Chanderbhan BE Final year Production amp Industrial

Engineering has submitted his seminar titled ldquoAUTONOMOUS MOBILE ROBOT

NAVIGATIONrdquo in the partial fulfillment of the requirement for the degree of

Bachelor of engineering production amp industrial in under my supervision and

guidance

DATE Dr AKVERMA

PLACE Associate Professor

Dept of production amp industrial engg

MBM Engineering College

Jai Narayan Vyas University

Jodhpur

ACKNOWLEDGEMENT

The successful completion of any task would be incomplete without the mention

of the people who made it possible and whose constant guidance and

encouragement crown all the efforts with success This Acknowledgement

transcends the reality of formality when I would like to express deep gratitude

and respect to all those people behind the screen who guided inspired and

helped me for the completion of this report work

I owe sincere thanks to Dr A K VERMA Associate Professor MBM Engg

College who gave an opportunity to work on this report work

The course of developing this report took a lot of determination and thoughts and

it was he who perspicuously devised the report and guided me to solve the

difficulties encountered during the report session

I also appreciate the efforts of my parents and friends who have contributed in

some form or the other in grooming me all through the tenure

CHANDERBHAN

ABSTRACT

Navigation methods based on stereo vision dead reckoning gradient rfid and neuro fuzzy are presented here They have been used in an autonomous mobile

robot which developed by the group of the author In stereo vision compass and encoders are used complementarily each other to get correct position 3D vision-based path-planning makes the robot walk along a better path each calculation cycle and avoid bumping other objects Dead reckoning navigation algorithm using a differential encoder and a gyroscope is proposed for an autonomous mobile robot (AMR) The analysis of global and local navigation Methods allowed to select the main lacks of existent methods of navigation The improved local navigation method based on the use of potential fields for movement taking into account the gradient of direction to the goal is proposed Radio Frequency Identification (RFID) is being increasingly used as an augmentation technology in the domain of environment mapping and ubiquitous computing We also present a novel method for localizing RFID tags embedded in indoor environments by using a mobile robot equipped with RF antennas and reader and a laser range finder Neuro fuzzy based systems are developed for behavior based control of a mobile robot for reactive navigation The proposed systems transform sensorsrsquo input to yield wheel velocities

Keywords- Mobile Robot Autonomous Navigation 3D Vision Robot Control Local Navigation Method Global Navigation Method Gradient Search Method AMR navigation Gyroscope Encoder Kalman filter Neural-Fuzzy system Behavior control Fuzzy membership functions

1 INTRODUTION 6-9

2 METHODS OF NAVIGATION 10-273 FUTURE OF RABOT 284 CONCLUSION 295 BIBLIOGRAPHY 30-32

INTRODUCTION

As humans we enjoy the luxury of having an amazing computer the brain and thousands of sensors to help navigate and interact with the real world The product of aeons of evolution has enabled our minds to model the world around us based on the information gathered by our senses In order to navigate successfully we can make high-level navigation decisions such as how to get

from point A to point B as well as low-level navigation decisions such as how to pass through a doorway The brains capacity to adapt has also made it possible for people without certain sensory capabilities to navigate throughout their environments For example blind people can maneuver through unfamiliar areas with the aid of seeing-eye dogs or canes Even without all of our sensors we are able to cope with familiar and unfamiliar environments when we refer to a robotrsquos intelligence a key problem is how to solve its navigation in real environments There have been a lot of researches focusing on navigation algorithm we are discussing briefly some navigation technique here

There have been a lot of researches focusing on navigation algorithm especially visual navigation which is regarded as the highest level algorithm There are four basic problems relative with navigation

(1) apperceivingmdashthe robot should interpret information from sensors and pick up useful data from them

(2) positioningmdashrobot should know its own position and orientation in its environment

(3) cognizingmdashthe robot should decide how to take action to achieve its goal

(4) motion controllingmdashthe robot should adjust its movement to get expected track In the above four problems positioning ability is the most basic problem for navigation

In most mobile robot applications two basic position estimation methods are employed together absolute positioning and relative positioning methods Absolute positioning methods usually rely on (a) navigation beacons (b) active or passive landmarks (c) map matching or (d) satellite-based navigation signals Each of these absolute positioning approaches can be implemented by a variety of methods and sensors Thought over above positioning methods we selected compass and assisted by encodes to do positioning work in the navigation system of our autonomous robots

Dead reckoning navigation system provides a position heading linear and angular velocity of an Autonomous Mobile Robot (AMR) and it is widely used for the AMR due to its simplicity and easy maintenance The most sophisticate form of the dead reckoning system is the inertial navigation system which uses inertial sensors such as gyroscopes accelerometers to measure the angular velocity and the linear acceleration with respect to the inertial space The angular velocity from a gyroscope is integrated to provide the heading and the linear acceleration from accelerometers is integrated to provide the velocity for the AMR The gyroscope and accelerometer measurements contain deterministic errors and stochastic errors Therefore estimation and filtering algorithm is necessary to correct those errors The recent development of inexpensive inertial sensors gives a way to broad applications of inertial sensors to the AMR navigation Extended Kalman filters have been studied to estimate the AMR position and heading using gyroscopes Our aims at developing a position and heading estimator for the AMR navigation system which is composed of a differential encoder and a gyroscope

Regular iterative method of the gradient search at the expense of local estimation of the second -order limits gets the subsequent progress It allows evaluating adaptive navigation of autonomous mobile robot in conditions of uncertainty and dynamic obstacles and increasing the probability of movement to the goal

In the last few years Radio Frequency Identification (RFID) has been receiving great attention as a promising technology for object identification and tracking with a wide range of applications Examples include inventory management industry automation ID badges and access control equipment and personnel tracking Compared to conventional identification systems such as barcodes RFID tags offer several advantages since they do not require direct line-of-sight and multiple tags can be detected simultaneously RFID systems typically consist of radio frequency (RF) tags a reader with one or more antennas and software to process the tag readings The reader interrogates the tags receiving their ID code and other information stored in their memory Tags can be either active or passive Active tags hold an internal power source methods for localizing

automatically the tags in the environment are therefore generally needed we present a novel approach to passive RFID tag localization by a mobile robot

Various approaches are found in literature for mobile robot navigation on neural and fuzzy based systems The approach considered neuro-fuzzy system architecture for behavior-based control of a mobile robot in unknown environments Another approach has described a reactive obstacle avoidance that enables robot to move in an unknown environment in which resultant velocity command to each wheel motion controller is generated through Fuzzy Kohonen Clustering Network (FKCN) instead of by conventional fuzzy Inference Humans have a remarkable capability to perform a wide variety of physical and mental task without any explicit measurements or computations Fuzzy logic provides a formal methodology for representing and implementing the human experts heuristic knowledge and perception based actions Our proposed systemrsquos conceptualization is analogous to that indicated in general terms

METHOD OF NAVIGATION

A Positioning and Navigation Algorithm of Autonomous Mobile Robot When we refer to a robotrsquos intelligence a key problem is how to solve its navigation in real environments There have been a lot of researches focusing on navigation algorithm especially visual navigation which is regarded as the highest level algorithm In the former studies a stereo vision navigation algorithm has been used in our robot There are four basic problems relative with navigation (1) apperceivingmdashthe robot should interpret information from sensors and pick up useful data from them (2) positioningmdash robot should know its own position and orientation in itsenvironment (3) cognizingmdashthe robot should decide how to take action to achieve its goal (4) motion controllingmdashthe robot should adjust its movement to get expected track In the above four problems positioning ability is the most basic problem for navigation Assume that when the robot is operating after it receives a command ldquomove from present position to the goalrdquo what action should it take Obviously it should know where itself is at first As a person because heshe have the sense of geography location heshe know where himself herself is in a room or where the building are Heshe does not have to know the coordinates of own position clearly but it is important for heshe to have the ability to remember the scenes and ability to distinguish own location For a mobile robot it is difficult to get the ability compared toa person A robotrsquos position expressed as numerical format and is processed in this format First set the coordinate system in a certain point of its environment then express its pose (position and orientation) concerning the coordinate system in numerical format In most mobile robot applications two basic position estimation methods are employed together absolute positioning and relative positioning methods Absolute positioning methods usually rely on (a) navigation beacons (b) active or passive landmarks (c) map matching or (d) satellite-based navigation signals Each of these absolute positioning approaches can be implemented by a variety of methods and sensors Yet none of the currently existing systems is particularly elegant Navigation beacons and landmarks usually require costly installations and maintenance while map-matching methods are either very slow or inaccurate or even unreliable With any one of these measurements it is necessary that the work environment either be prepared or be known and

mapped with great precision Satellite-based navigation (GPS) can be used only outdoors and useless for robots walking indoors

MAPPING

path-planning based on stereo vision Relative positioning is usually based on dead-reckoning (ie monitoring the wheel revolutions to compute the offset from a known starting position) Dead-reckoning is simple inexpensive and easy to accomplish in real-time The disadvantage of dead-reckoning is its unbounded accumulation of errors Another approach to the position determination of mobile robots is based on inertial navigation with gyros andor accelerometers It can lessen accumulation of errors but these sensors are exceedingly sensitive to drift and any small drift will be enlarged by accumulating Electronic compasses can determine the local vector toward the north magnetic pole so it has no accumulated errors and it can avoid the sensorrsquos drift problem of inertial navigation Moreover compasses are easily and cheap to install in robots

Dead Reckoning Navigation for an Autonomous Mobile Robot Using a Differential Encoder and a Gyroscope Encoder navigation system is basically a dead reckoning navigation system which provides a position heading linear and angular velocity of an Autonomous Mobile Robot(AMR) and it is widely used for the AMR due to its simplicity and easy maintenance The advantages of the encoder navigation system are that the encoders are inexpensive and provide relatively accurate information when the encoder errors are carefully calibrated However it is apparent that the encoder errors will have an effect on both the heading and the position of the AMR according to the moving distance The most sophisticate form of the dead reckoning system is the inertial navigation system which uses inertial sensors such as gyroscopes accelerometers to measure the angular velocity and the linear acceleration with respect to the inertial space The angular velocity from a gyroscope is integrated to provide the heading and the linear acceleration from accelerometers is integrated to provide the velocity for the AMR The gyroscope and accelerometer measurements contain deterministic errors and stochastic errors Therefore estimation and filtering algorithm is necessary to correct those errors The recent development of inexpensive inertial sensors gives a way to broad applications of inertial sensors to the AMR navigation Extended Kalman filters have been studied to estimate the AMR position and heading using gyroscopes As the previous researches aim to estimate the position and heading of an AMR the systematic errors of the encoder and the stochastic errors of the gyroscope have not been considered

explicitly The previous algorithms compensate only the deterministic error of the gyroscope

Our research aims at developing a position and heading estimator for the AMR navigation system which is composed of a differential encoder and a gyroscope As the dead reckoning navigation requires the accurate information of an encoder and a gyroscope the systematic errors of the differential encoder and the stochastic errors of the gyroscope should be estimated explicitly using a Kalman filter The previous algorithms using EKF estimate the position and heading but our proposed indirect Kalman filter estimates and compensates the errors of the differential encoder and the gyroscope instead Moreover the navigation system can use the unfiltered position and heading of AMR when the filter fails since the indirect Kalman filter does not directly estimate the position and heading of the AMR

In the section the AMR navigation system using a differential encoder a gyroscope and the indirect Kalman filter is designed the proposed navigation system performance is evaluated through the experiments

Gradient Method for Autonomous Robot Navigation Regular iterative method of the gradient search at theexpense of local estimation of the second -order limits gets the subsequent progress It allows to evaluate adaptive navigation of autonomous mobile robot in conditions of uncertainty and dynamic obstacles and increase the probability of movement to the goal

FORMULATION OF RESEARCH TASK Environment for autonomous mobile robot (AMR) functioning can be divided on the two types structured (known) and unstructured (complex un known) Taking into account the type of functioning environment the local and global navigation methods are exist If the environment is known and the goal is placed into known environment then global navigation methods is applied for mobile robot navigation If the environment is unknown or robot should perform exploration of an environment then local navigation methods which used only local information of environment which is taken by using methods of local area map building is applied There are many implementations of AMR control system which use global navigation methods In particular Visibility Graph Voronoi Diagrams Quartrees Wave Front In general algorithm of global navigation methods consists of two stages1048585 planning of trajectory of mobile robot movement1048585 direct movement to the goal using the information about necessary trajectory which was determine on the first stage After the analysis of known global navigation methods it is possible to indicate the dis advantages which appeared while such methods are using1048585 computing complexity of methods for large local area maps and many obstacles1048585 necessity often perform the localization procedure due to inaccuracy of sensor and odometric robot systems but it is the separate task which is a reason of increasing more computing complexity Therefore it is necessary to use methods which have less computing complexity for example methods of local navigation In contrast to the global navigation methods the local navigation methods use sensor information for robot movement to the predetermined goal In this case when global area map is unknown or environment is unstructured or has a lot of dynamic obstacles

the application of global navigation methods is impossible The most famous local navigation method which is based on use of sensor information about environment is BUG There are many of its modifications PolarBUG VisBUG FuzzyBUG now One more approach which is used in local navigation methods is using of the Potential Fields of objects in the environment After analysis of known local navigation methods it is possible to indicate the disadvantages which appeared while such methods are using1048585 more complex problem of robot localization compare with global navigation methods1048585 deviation from optimal path of movement1048585 reaching to local minimum (blocking obstacles)1048585 looping (going round same trajectory) while attempt to leave local minimum The analysis of known local and global navigation methodsshowed that in the present time there are no efficient engineering solutions which allow AMR to reach goal when the insignificant changes of environment are present in the global navigation and the deadlock situations are present in the local navigation The proposed local navigation method allows to perform criteria to reach the goal during AMR movement in complex unstructured environment Also it pr ovides the AMR navigation between dynamic obstacles or obstacles which are not shown on the global area map The known global navigation methods cannot provide the reaching to the goal in the environment with obstacles which are not shown on the global area map The advantage of proposed method compare with known local navigation method is possibility of exit from local minimums Its possibility is provided by second stage of method

namely the stage of obstacles avoidance along the perimeter

Graphical presentation of influence of potential fields of two obstacles Graphical

RFID-Based Environment Mapping for Autonomous Mobile RobotApplications In the last few years Radio Frequency Identification (RFID) has been receiving great attention as a promising technology for object identification and tracking with a wide range of applications Examples include inventory management industry automation ID badges and access control equipment and personnel tracking Compared to conventional identification systems such as barcodes RFID tags offer several advantages since they do not require direct line-of-sight and multiple tags can be detected simultaneously Recently RFID technology has been introduced in the field of mobile robotics Attached to walls machines or other specific places in the environment RFID tags make the robot able to detect items obtain information about its position and even get instructions to reach a given goal Moreover although infrastructure preparations are needed these are simple and RFID tags can be placed almost anywhere a landmark is required Hence RFID constitute potentially an effective support to navigation of autonomous mobile robots in indoor environments RFID

systems typically consist of radio frequency (RF) tags a reader with one or more antennas and software to process the tag readings The reader interrogates the tags receiving their ID code and other information stored in their memory reliable than passive devices but they are expensive and have a limited lifetime That makes them unsuitable for applications dealing with several tags Conversely passive tags operate without a battery since they are activated by the electromagnetic field generated by the RFID antenna They are quite small and cheap However passive tags have a critical limitation in that they can just provide their identity whereas they have no notion of their own location On the other hand the knowledge of the tag position is needed in many applications such as in robotic control systems Most of existing RFID solutions assume the position of the tags to be known a priori more or less accurately This hypothesis is reasonable in some industrial applications while in office or home environments it is usually difficult to measure tag locations Furthermore objects holding a tag could be displaced causing the necessity to recalculate their position Methods for localizing automatically the tags in the environment are therefore generally needed Recent advances in the field of radio frequency technology have contributed to a large diffusion of RFIDbased systems Currently low-cost passive tags that can be detected in the range of several meters are commercially available That makes RFID suitable for mobile robotics tasks such as localization and mapping There are several works in literature that investigate the use of RFID in mobile robotics For instance radio frequency identification tags are employed as artificial landmarks for mobile robot navigation based on a topological map An RFID-based robotic system for visually impaired assistance is developed It uses passive RFID tags manually attached to objects in an indoor environment to trigger local navigation behaviors of a mobile robot While these methods are all effective in supporting mobile robot navigation they mostly assume the location of the tags to be known a-priori Actually it is usually difficult to fix a tag in a predetermined place or to measure its location Solutions to the problem of automatic tag localization are proposed based on Bayesian schemes and a simplified antenna model In this work we investigate an alternative approach for localizing passive tags in the environment We employ a mobile robot equipped with an RFID system and a laser range finder and refer to a model of the antenna reading range for tag location estimate Our approach however is unique in that it uses fuzzy reasoning for both learning a model of the RFID system and locating the tags Tag positions are referred to a map of the

environment constructed using laser data We show that such map can be effectively employed for mobile robot navigation tasks

Antenna detection field Darker blue indicates higher detection frequency

RFID tags localized in a laser-based map of the environment

Fuzzy model of the RFID antenna (a)-(b) input membership functions (c) output levels

Fuzzy inference system for tag localization (a)-(b) input and (c) output membership functions

Neuro-Fuzzy Based Autonomous Mobile Robot Navigation System Autonomous robot navigation means the ability of a robot to move purposefully and without human intervention in environments that have not been specifically engineered for it Autonomous navigation requires a number of heterogeneous capabilities like ability to reach a given location to reach in real time to unexpected events to determine the robots position and to adapt to changes in the environment For a mobile robot to navigate automatically and rapidly an important factor is to identify and classify mobile robotsrsquo perceptual environment The general theory for mobile robotics navigation is based on a following idea the robot must Sense the known world be able to Plan its operations and then Act based on the model Various approaches are found in literature for mobile robot navigation including neural and fuzzy based systems The approach considered neuro-fuzzy system architecture for behavior-based control of a mobile robot in unknown environments Another approach has described a reactive obstacle avoidance that enables robot to move in an unknown environment in which resultant velocity command to each wheel motion controller is generated through Fuzzy Kohonen Clustering Network (FKCN)

instead of by conventional fuzzy inference Several other methods exploiting fuzzy control schemes have been proposed for avoiding unexpected obstacles Humans have a remarkable capability to perform a wide variety of physical and mental task without any explicit measurements or computations Fuzzy logic provides a formal methodology for representing and implementing the human experts heuristic knowledge and perception based actions Our proposed systemrsquos conceptualization is analogous to that indicated in general terms byhelliphellip while our actual detailed system is new

Range Calculation of a Mobile Robot from given Obstacles

Training using neural network Training of intelligent system is crucial for successful navigation of mobile vehicle Training is difficult in the sense that input space may contain infinitely many possibilities mobile robot need to learn effectively Many times mobile robot needs to execute operations in hazardous environments like fire or space missions where online training is not feasible Off line training is possible in such cases Mobile robot needs to sense environment in real time and also to make precise decision based on learning Few training approaches are found in literature ie a) generating training sequences by experimental set up and b) heuristic approach based on expert rules In the first approach (training by

experimental setups) learning is done by setting different environmental set ups ie different start end (target ) positions different obstacles positions etc In this case number of training pairs resulted for different input pairs may not be evenly distributed Some of the input pairs may appear more number of times while some may appear lesser or even not appear Training may not be considered optimum as for some inputs patterns are not learnt while some are over learnt In case of second alternative (training by expert rules) training is done by fewer number of input patterns This type of training may save training time may give good performance in some cases but they may not perform well in all kind of environmental conditions This is because of the fact that selection of training pairs is for particular task and they do not represent entire space uniformly Hence their output in unexplored space of input space is not guaranteed We propose mobile robotrsquos training based on uniform sampling that overcomes the problems with above mentioned methods The proposed algorithms not only takes samples from entire sample space (to provide heterogeneity) also takes equal number of sample data from all possible input space (to provide homogeneity) In the proposed algorithm actual sensor readings are considered to be quantized in to n linguistic values Uniform sampling of these quantized values will enable us a) to consider entire space of input region and b) will enable us to generate optimum number of training pairs required for training In the proposed approach we train the network as follows1 First let input cardinality (number of sensor inputs) of the neural networks equal to m Also assume that each input takes n linguistic values (eg near medium far) Then we can generate total nm training pairs2 Second output values of each of these input patterns are decided based on experimentation or by expert rules3 Neural network is trained accordingly to training pairs generated and performance of the network can be checked using proper evaluating function eg MSE (mean square error)4 If any correction is required make adjustment to step 2 and then repeat steps

Membership functions regarding input output variables

These fuzzy rules show that the robot mainly adjusts its motion direction and quickly moves to the target if there are no obstacles around the robot When the acquired information from the ultrasonic sensors shows that there are no obstacles to the left front or right of robot its main reactive behavior is target steer When the acquired information from the ultrasonic sensors shows that there exist obstacles nearby robot it must try to change its path in order to avoid those obstacles When the robot is moving to a specified target inside a room or escaping from a U-shaped obstacle it must reflect following edge behavior

Comparison of Robot Navigation with Neuro-Fuzzy System(NFS) to Neural and Fuzzy System the path comparison of a mobile robot between single stage neural and fuzzy approaches while the mobile robot path comparison between neural and proposed neuro-fuzzy systems These results suggest that in the case of second stage (driving stage) fuzzy systems are preferred This is because neural networkrsquos output in the unexplored regions of inputs is not

predictable and error at each stage get accumulated and hence do not give good stable paths Robot eventually strikes the obstacles located to the left bottom corner while with the same scenario in the case of neuro fuzzy system successfully avoids the same obstacle These is because in the case of single stage fuzzy systems that one of the inputs (ie heading angle) contradicts to the perception by the other inputs while in the case of neuro-fuzzy system computing reference heading angle (RHA) suggest more practical input to the fuzzy system of the second stage Neurondash Fuzzy system architecture uses neural network to the input side of Fuzzy system for understanding environment This is because to understand higher dimensional complex environment neural network having point to point mapping performs more efficiently than fuzzy systems that has set to set mapping These simulation results highlight the fact that adding neural stage to the input side enhances environmental 387 sensing capacity to the fuzzy system The same fact is observed for multiple simulations done with various environmental conditions

Comparison of Robot navigation Neural amp Fuzzy system

Robot navigation with Two stage Neuro- Fuzzy system In this paper a new approach for robot navigation algorithms neuro-fuzzy based systems is discussed The mobile robot performs reactive navigation and suitable for real time dynamic environment rather than looking for optimal path as performed by path planning techniques Simulation results for mobile robot navigation with neuro-fuzzy based system demonstrate the good performance in complex and unknown environments navigated by the mobile robot Simulation results suggest that information on environment (Sense) should be obtained by neural networks while more correct decisions (Act) should be made by the use of fuzzy systems In future algorithms may be developed for multiple 388 robots cases and comparison can be done for more neuro fuzzy based approaches

FUTURE OF ROBOTICS Forget GPS and streaming video mdash future legions of city-dwelling robots may navigate using manhole covers The ubiquitous round metallic covers each have different shapes and sizes occasionally for the sake of aesthetics and certainly when you account for wear and tear In Japan manhole covers are frequently works of art reflecting something about their cities And every city has them mdash theyrsquore one of the more permanent reliable fixtures of the built environment as New Scientist point Hajime Fujii and colleagues from Shibaura Institute of Technology in Tokyo say robots could take advantage of this and use the covers to estimate their positions All you would need is a basic metal detector attached to a robotrsquos foot Other robot-navigation methods use GPS laser-range scans and even CCD cameras that compare a robotrsquos view to maps or even Google Street View But environmental factors can skew the data from these sources Fujii writes mdash GPS isnrsquot always reliable in cities and Street View may not be so not helpful at night Maps are helpful but robots would still need to check their position against some kind of environmental landmark

In Fujiirsquos system its as simple as manhole covers Every cover would be scanned and its shape would be entered into a database for each city Robots would be able to find the covers using a metal detector and swipe some kind of scanner across the covers to cross-check the database and figure out where they are Of course this would require robots stepping into traffic to check their whereabouts But when we all have flying cars that wont matterFuture-Shape is specialized in large-area contactless sensor systems with a variety of possible applications All conductive surfaces are suitable as sensor planes and can be combined in nearly arbitrary form and number into a sensitive area with a high spatial resolution The sensor data are transmitted wirelessly and can be evaluated in different ways according to the aspired application

CONCLUSION In this paper navigation methods based on stereo vision dead reckoning gradient rfid and neuro fuzzy are presented they have been used in an autonomous mobile robot which developed by the group of the author The compass reduces possible accumulated errors of dead-reckoning and encoders correct possible great compass errors Based on the stereo camera depth information helps the robot take a better path each cycle and avoid bumping others During running the position of the robot is compared with the position of goal when their difference is less than the preset threshold robot will stopThe proposed AMR navigation system basically depends on the encoder and calibrates the encoder errors using the gyroscope The systematic errors of the differential encoder and the stochastic gyroscope errors have been modeled and included in the navigation filter Instead of using an extended Kalman filter an indirect Kalman filter is adopted It was shown that the method is effective in localizing RFID tags and can be successfully used for robot navigation and environment mapping applicationsnew approach for robot navigation algorithms neuro-fuzzy based systems is also discussed The mobile robot performs reactive navigation and suitable for real time dynamic environment rather than looking for optimal path as performed by path planning techniques Simulation results for mobile robot navigation with neuro-fuzzy based system demonstrate the good performance in complex and unknown environments navigated by the mobile robot Simulation results suggest that information on environment (Sense) should be obtained by neural networks while more correct decisions (Act) should be made by the use of fuzzy systems

REFERENCES[1] A Positioning and Navigation Algorithm of Autonomous Mobile Robot Qiuhong LU Shaoyuan LI GuozhengYAN School of Electronics Information and Electrical Engineering Shanghai Jiao Tong University Shanghai Chinaluqiuhong1sinacom978-1-61284-459-611$2600 copy2011 IEEE

[2] Dead Reckoning Navigation for an Autonomous Mobile Robot Using a Differential Encoder and a GyroscopeKyucheol Park Hakyoung Chung Jongbin Choi and Tang Gyu Lee Automatic Control Research Center School of Electrical Engineering Seoul National University Seoul 151-742 Korea Department of Control and Instrumentation Engineering Seoul National Polytechnic University Seou1139-743 Korea0-7803-41 60-0-797 $1000 0 1997 IEEE

[3]Gradient Method for Autonomous Robot Navigation Oleh Adamiv Anatoly Sachenko Viktor Kapura Ternopil National Economic University Research Institute of Intelligent Computer Systems 3 Peremoga Square Ternopil 46004 Ukraine icstaneteduteuaTCSET2008 February 19-23 2008 Lviv-Slavsko Ukraine

[4] RFID-Based Environment Mapping for Autonomous Mobile Robot ApplicationsManuscript received January 15 2007 A Milella P Vanadia G Cicirelli and A Distante are with the Institute of Intelligent Systems for Automation (ISSIA) National Research Council (CNR) via G Amendola 122D-O 70126 Bari Italy Corresponding Author Annalisa Milella e-mail milellabaissiacnrit1-4244-1264-107$2500 copy2007 IEEE

[5] Neuro-Fuzzy Based Autonomous Mobile Robot Navigation SystemMaulin MJoshi Department of Electronics amp Comm Engineering Sarvajanik College of Engg amp Technology Surat India maulinjoshiscetacin Mukesh AZaveri Department of Computer Engineering Sardar Vallabhbhai National Institute of Technology Surat India mazavericoedsvnitacin978-1-4244-7815-610$2600 copy2010 IEEE

This is to certify that Mr Chanderbhan BE Final year Production amp Industrial

Engineering has submitted his seminar titled ldquoAUTONOMOUS MOBILE ROBOT

NAVIGATIONrdquo in the partial fulfillment of the requirement for the degree of

Bachelor of engineering production amp industrial in under my supervision and

guidance

DATE Dr AKVERMA

PLACE Associate Professor

Dept of production amp industrial engg

MBM Engineering College

Jai Narayan Vyas University

Jodhpur

ACKNOWLEDGEMENT

CHANDERBHAN

ABSTRACT

1 INTRODUTION 6-9

INTRODUCTION

MAPPING

CHANDERBHAN

ABSTRACT

1 INTRODUTION 6-9

INTRODUCTION

MAPPING

1 INTRODUTION 6-9

INTRODUCTION

MAPPING

INTRODUCTION

MAPPING

Documents

Final report on autonomous mobile robot navigation