A Fuzzy Logic Based Navigation of a Mobile Robot

One of the long standing challenging aspect in mobile robotics is the ability to navigate autonomously, avoiding modeled and unmodeled obstacles especially in crowded and unpredictably changing environment. A successful way of structuring the navigation task in order to deal with the problem is within behavior based navigation approaches. In this study, Issues of individual behavior design and action coordination of the behaviors will be addressed using fuzzy logic. A layered approach is employed in this work in which a supervision layer based on the context makes a decision as to which behavior(s) to process (activate) rather than processing all behavior(s) and then blending the appropriate ones, as a result time and computational resources are saved. Keywords—Behavior based navigation, context based coordination, fuzzy logic, mobile robots.

[1]  Rodney A. Brooks,et al.  A Robust Layered Control Syste For A Mobile Robot , 2022 .

[2]  A. S. otti The Uses of Fuzzy Logicin Autonomous Robot Navigation : a catalogue raisonn , 2007 .

[3]  Martial Hebert,et al.  A behavior-based system for off-road navigation , 1994, IEEE Trans. Robotics Autom..

[4]  Emmanuel G. Collins,et al.  Implementation of Multi-valued Fuzzy Behavior Control for Robot Navigation in Cluttered Environments , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[5]  Antonio González Muñoz,et al.  Fuzzy behaviors for mobile robot navigation: design, coordination and fusion , 2000, Int. J. Approx. Reason..

[6]  Ronald C. Arkin,et al.  Motor Schema — Based Mobile Robot Navigation , 1989, Int. J. Robotics Res..

[7]  Venansius Baryamureeba,et al.  PROCEEDINGS OF WORLD ACADEMY OF SCIENCE, ENGINEERING AND TECHNOLOGY, VOL 8 , 2005 .

[8]  Alessandro Saffiotti,et al.  The uses of fuzzy logic in autonomous robot navigation , 1997, Soft Comput..

[9]  Xiaoyu Yang,et al.  A layered goal-oriented fuzzy motion planning strategy for mobile robot navigation , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[10]  Rodney A. Brooks,et al.  A robot that walks; emergent behaviors from a carefully evolved network , 1989, Proceedings, 1989 International Conference on Robotics and Automation.

[11]  Henrik I. Christensen,et al.  Behaviour coordination for navigation in office environments , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[12]  Edward Tunstel,et al.  Behavior Hierarchy for Autonomous Mobile Robots: Fuzzy-Behavior Modulation and Evolution , 1997, Intell. Autom. Soft Comput..

[13]  Julio Rosenblatt,et al.  DAMN: a distributed architecture for mobile navigation , 1997, J. Exp. Theor. Artif. Intell..

[14]  P. Maes How to Do the Right Thing , 1989 .

[15]  Maja J. Matari,et al.  Behavior-based Control: Examples from Navigation, Learning, and Group Behavior , 1997 .

[16]  Homayoun Seraji,et al.  Behavior-based robot navigation on challenging terrain: A fuzzy logic approach , 2002, IEEE Trans. Robotics Autom..

[17]  Edward M. Riseman,et al.  Towards cosmopolitan robots: intelligent navigation in extended man-made environments , 1987 .

[18]  Tucker R. Balch,et al.  AuRA: principles and practice in review , 1997, J. Exp. Theor. Artif. Intell..

[19]  Peter Xiaoping Liu,et al.  An embedded fuzzy controller for a behavior-based mobile robot with guaranteed performance , 2004, IEEE Transactions on Fuzzy Systems.

[20]  Homayoun Seraji,et al.  Terrain-Based Navigation of Planetary Rovers: A Fuzzy Logic Approach , 2001 .

[21]  Maja J. Mataric,et al.  Behaviour-based control: examples from navigation, learning, and group behaviour , 1997, J. Exp. Theor. Artif. Intell..

[22]  J. K. Rosenblatt,et al.  A fine-grained alternative to the subsumption architecture for mobile robot control , 1989, International 1989 Joint Conference on Neural Networks.