A Behavior Based Control and Learning Approach to Real Robots

Programming a real robot to do a given task in unstructured dynamic environments is very challenge. Incomplete information, large learning space, and uncertainty are major obstacles for control in real robots. When programming a real robot in unstructured dynamic environments, it is impossible to predict all the potential situation robots may encounter and specify all robot behaviors optimally in advance. Robots have to learn from, and adapt to their operating environment.

[1]  Ronald C. Arkin,et al.  An Behavior-based Robotics , 1998 .

[2]  Andrew W. Moore,et al.  Reinforcement Learning: A Survey , 1996, J. Artif. Intell. Res..

[3]  A. Safiotti,et al.  Fuzzy logic in autonomous robotics: behavior coordination , 1997, Proceedings of 6th International Fuzzy Systems Conference.

[4]  Roderic A. Grupen,et al.  A hybrid architecture for adaptive robot control , 2000 .

[5]  D.P. Filev,et al.  An approach to online identification of Takagi-Sugeno fuzzy models , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[6]  Peter Stone,et al.  Layered Learning in Multiagent Systems , 1997, AAAI/IAAI.

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

[8]  Chris Watkins,et al.  Learning from delayed rewards , 1989 .

[9]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[10]  Xiao Peng,et al.  Fuzzy behavior-based control of mobile robots , 2004, IEEE Transactions on Fuzzy Systems.

[11]  Emil M. Petriu,et al.  Behavior-based neuro-fuzzy controller for mobile robot navigation , 2003, IEEE Trans. Instrum. Meas..

[12]  Weiliang Xu,et al.  Sensor-based fuzzy reactive navigation of a mobile robot through local target switching , 1999, IEEE Trans. Syst. Man Cybern. Part C.