it is a navigation report on robot navigation....
A SEMINAR REPORT ON AUTONOMOUS MOBILE ROBOT NAVIGATION
DEPARTMENT OF PRODUCTION AND INDUSTRIAL ENGG. M.B.M. ENGINEERING COLLEGE
(Submitted in the partial fulfillment of B.E. degree in P&I)
SUBMITTED BY:
GUIDED BY:
CHANDERBHAN
DR. A. K. VERMA
B.E. Final Year
ASSOCIATE PROFESSOR
E.No. 08/01325 Roll No. 2216004
CERTIFICATE 1
This is to certify that Mr. Chanderbhan, BE Final year, Production & Industrial Engineering, has submitted his seminar titled NAVIGATION in
AUTONOMOUS MOBILE ROBOT
the partial fulfillment of the requirement for the degree of
Bachelor of engineering production & industrial in under my supervision and guidance.
DATE:
Dr. A.K.VERMA
PLACE:
Associate Professor Professo r Dept. of production & industrial engg. MBM Engineering College Jai Narayan Vyas University Jodhpur
ACKNOWLEDGEMENT
2
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, M.B.M. 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 3
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 visionbased path-planning makes the robot walk along a better path each calculation cycle and avoid avoid bumping other objects. Dead reckoning 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 sensors 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
INDEX
1. INTRODUTION
6-9 4
2.
METHODS OF NAVIGATION
10-27
3.
FUTURE OF RABOT
28
4.
CONCLUSION
29
5.
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
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from point A to point B, as well as low-level navigation decisions, such as how to pass through a doorway. The brain's 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 robots 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) apperceivingthe robot should interpret information from sensors and pick up useful data from them; (2) positioningrobot should know its own position and orientation in its environment; (3) cognizingthe robot should decide how to take action to achieve its goal; (4) motion controllingthe 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.
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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 lineof-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 7
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 neurofuzzy 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 expert's heuristic knowledge and perception based actions. Our proposed systems conceptualization is analogous to that indicated in general terms.
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METHOD OF NAVIGATION
A Positioning and Navigation Algorithm of Autonomous Mobile Robot When we refer to a robots 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) apperceivingthe robot should interpret information from sensors and pick up useful data from them; (2) positioning robot should know its own position and orientation in its environment; (3) cognizingthe robot should decide how to take action to achieve its goal; (4) motion controllingthe 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 move from present position to the goal, what action should it take? Obviously, it should know where itself is at first. As a person, because he/she have the sense of geography location, he/she know where himself /herself is in a room, or where the building are. He/she does not have to know the coordinates of own position clearly, but it is important for he/she 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 to a person. A robots 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 9
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 deadreckoning (i.e., 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 and/or 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 sensors drift problem of inertial navigation. Moreover, compasses are easily and cheap to install in robots.
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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 11
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.
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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.
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Gradient Method for Autonomous Robot Navigation Regular iterative method of the gradient search at the expense 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 stages: planning of trajectory of mobile robot movement; 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 using: computing complexity of methods for large local area maps and many obstacles; 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 14
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 using: more complex problem of robot localization compare with global navigation methods; deviation from optimal path of movement; reaching to local minimum (blocking obstacles); looping (going round same trajectory) while attempt to leave local minimum. The analysis of known local and global navigation methods showed 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,
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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 Robot Applications 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 16
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 17
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 18
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Fuzzy model of the RFID antenna: (a)-(b) input membership functions; (c) output levels.
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21
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 robot's 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 robots 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) 22
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 expert's heuristic knowledge and perception based actions. Our proposed systems conceptualization is analogous to that indicated in general terms by by;; 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 i.e. a) generating training sequences by experimental set up and b) heuristic approach based on expert rules. In the first approach (training by 23
experimental setups), learning is done by setting different environmental set ups. i.e. 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 robots 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 follows: 1. First, let input cardinality (number of sensor inputs) of the neural networks equal to m. Also, assume that each input takes n linguistic values (e.g. near, medium, far). Then we can generate total nm training pairs. 2. Second, output values of each of these input patterns are decided based on experimentation or by expert rules. 3. Neural network is trained accordingly to training pairs generated and performance of the network can be checked using proper evaluating function e.g. MSE (mean square error) 4. If any correction is required; make adjustment to step 2 and then repeat steps.
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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 networks output in the unexplored regions of inputs is not 25
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 (i.e. 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. Neuro 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 & Fuzzy system
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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.
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FUTURE OF ROBOTICS Forget GPS and streaming video future legions of citydwelling 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 theyre 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 robots foot. Other robot-navigation methods use GPS, laser-range scans and even CCD
cameras that compare a robots view to maps or even Google Street View. But environmental factors can skew the data from these sources, Fujii writes GPS isnt 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 Fujiis system, it's 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 won't matter! Future-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.
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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 stop.The 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 applications.new 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.
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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, China
[email protected] 978-1-61284-459-6/11/$26.00 ©2011 IEEE
[2] Dead Reckoning Navigation for an Autonomous Mobile Robot Using a Differential Encoder and a Gyroscope Kyucheol Park ' , Hakyoung Chung , Jongbin Choi and Tang 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, Seou1,139-743, Korea 0-7803-41 60-0-7/97 $10.00 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,
[email protected] TCSET'2008, February 19-23, 2008, Lviv-Slavsko, Ukraine [4] RFID-Based Environment Mapping for Autonomous Mobile Robot Applications,Manuscript 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 122/D-O, 70126, Bari, Italy. *Corresponding Author: Annalisa Milella; e-mail:
[email protected] 1-4244-1264-1/07/$25.00 ©2007 IEEE [5] Neuro-Fuzzy Based Autonomous Mobile Robot Navigation System,Maulin M.Joshi Department of Electronics & Comm. Engineering, Sarvajanik College of Engg. & Technology, Surat, India
[email protected] Mukesh A.Zaveri Department of Computer Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, India
[email protected] 978-1-4244-7815-6/10/$ 26.00 ©2010 IEEE
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