On-line Affective Cognitive Learning and Decision-making for Autonomous Navigation of Mobile Robots
A new autonomous navigation control framework is presented for mobile robots by integrating affective cognitive learning and decision making (ACLDM) model with behaviorbased robot system. Cognitive states for work environment of mobile robot are gotten from a pattern classifier based on Adaptive Resonance Theory-2 (ART-2) network. Then rational strategies for behaviors coordination are developed by on-line affective cognitive learning. The behaviors of robot navigation are designed by dynamic system approach. This control strategy can make the mobile robot navigate autonomously and safely in unknown environment. Simulation studies have demonstrated that the integration of the affective system with cognitive system can speed up the learning process, and the proposed strategy can effectively improve the capability of robots autonomous navigation in unknown environment.
Huidi Zhang Shirong Liu
Ningbo Institute of Technology,Zhejiang University,Ningbo 315100,China Hangzhou Dianzi University,Xiasha,Hangzhou 310018,China
国际会议
2009 IEEE International Conference on Information and Automation(2009年 IEEE信息与自动化国际学术会议)
珠海、澳门
英文
1234-1239
2009-06-22（万方平台首次上网日期，不代表论文的发表时间）