Situated learning of a behavior-based mobile robot path planner
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In this paper, we propose a behavior-based path planner that can self-learn in an unknown environment. A situated learning algorithm is designed which allows the robot to learn to coordinate several concurrent behaviors and improve its performance by interacting with the environment. Behaviors are implemented using CMAC neural networks. A simulation environment is set up and some simulation experiments are carried out to rest our learning algorithm.
KeywordsSituated learning behavior-based path planning CMAC neural networks
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