Reinforcement learning in a rule-based navigator for robotic manipulators

This paper reports on a navigation system for robotic manipulators. The control system combines a repelling influence related to the distance between manipulator and nearby obstacles with the attracting influence produced by the angular difference between actual and final manipulator configuration to generate actuating motor commands. The use of fuzzy logic for the implementation of these behaviors leads to a transparent system that can be tuned by hand or by a learning algorithm. The proposed learning algorithm, based on reinforcement-learning neural network techniques, can adapt the navigator to the idiosyncratic requirements of particular manipulators, as well as the environments they operate in. The navigation method, combining the transparency of fuzzy logic with the adaptability of neural networks, has successfully been applied to robot arms in different environments.

[1]  Nadine N. Tschichold-Gürman The neural network model RuleNet and its application to mobile robot navigation , 1997, Fuzzy Sets Syst..

[2]  S. Roberts,et al.  Regularisation of RBF-networks with the Bayesian evidence scheme , 1999 .

[3]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[4]  Michio Sugeno,et al.  An introductory survey of fuzzy control , 1985, Inf. Sci..

[5]  E. H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Man Mach. Stud..

[6]  M. Sugeno,et al.  Fuzzy parking control of model car , 1984, The 23rd IEEE Conference on Decision and Control.

[7]  Andrew G. Barto,et al.  Reinforcement learning control , 1994, Current Opinion in Neurobiology.

[8]  Shigeo Abe,et al.  Neural Networks and Fuzzy Systems , 1996, Springer US.

[9]  Kaspar Althoefer,et al.  Fuzzy Navigation for Robotic Manipulators , 1998, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[10]  Brian R. Gaines,et al.  Fuzzy reasoning and its applications , 1981 .

[11]  Frank Klawonn,et al.  Foundations of fuzzy systems , 1994 .

[12]  Richard S. Sutton,et al.  Planning by Incremental Dynamic Programming , 1991, ML.

[13]  William A. Wolovich,et al.  Robotics - basic analysis and design , 1987, HRW Series in electrical and computer engineering.

[14]  Geoffrey E. Hinton,et al.  Feudal Reinforcement Learning , 1992, NIPS.

[15]  Kai-Tai Song,et al.  Fuzzy Navigation Of A Mobile Robot , 1992, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems.

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

[17]  O. Khatib,et al.  Real-Time Obstacle Avoidance for Manipulators and Mobile Robots , 1985, Proceedings. 1985 IEEE International Conference on Robotics and Automation.

[18]  Jean-Claude Latombe,et al.  Robot motion planning , 1970, The Kluwer international series in engineering and computer science.

[19]  Patrick Reignier,et al.  Fuzzy logic techniques for mobile robot obstacle avoidance , 1994, Robotics Auton. Syst..