Affective Cognitive Learning and Decision Making and Application in Behavior Coordination for Mobile Robots

Abstract

Affective cognitive learning and decision making model is a new reinforcement learning,where intrinsic reward from emotion and extrinsic reward from cognition serve as motivation in learning and decision making.A new control strategy is presented in this paper for navigation of mobile robots by integrating affective cognitive learning and decision making model into behavior-based robot system.(Rational) strategies for behaviors coordination are developed by on-line affective cognitive learning.Thus,the autonomous navigation of mobile robot is realized effectively.Simulation studies demonstrate that the (integration) of the affective system can enhance the learning speed,and the proposed strategy can effectively(improve) the capability of robot's autonomous navigation under unknown environment.