Based on A* and Q-Learning Search and Rescue Robot Navigation

Tao Pang, Xiaogang Ruan, Ershen Wang, Ruiyuan Fan


For the search and rescue robot navigation in unknown environment, a bionic self-learning algorithm based on A* and Q-Learning is put forward. The algorithm utilizes the Growing Self-organizing Map (GSOM) to build the environment topology cognitive map. The heuristic search A* algorithm is used the global path planning. When the local environment changes, Q-Learning is used the local path planning. Thereby the robot can obtain the self-learning skill by studying and training like human or animal, and looks for a free path from the initial state to the target state in unknown environment. The theory proves the validity of the method. The simulation result shows the robot obtains the navigation capability.



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