Autonomous Path Planning Scheme Research for Mobile Robot

Jianxian Cai 1 , 2 , Xiaogang Ruan 1 , 3 , and Pengxuan Li 2
  • 1 School of Electronic and Control Engineering, Beijing University of Technology, No 100, Pingleyuan, Chaoyang District, 100124 Beijing, China China
  • 2 Department of Disaster Prevention Instrument, Institute of Disaster Prevention, No 3, University Street, Yanjiao Development Zone, Sanhe, 065201 Hebei, China
  • 3 Beijing Key Laboratory of Computational Intelligence and Intelligent System, No 100, Pingleyuan, Chaoyang District, 100124 Beijing, China


An autonomous path-planning strategy based on Skinner operant conditioning principle and reinforcement learning principle is developed in this paper. The core strategies are the use of tendency cell and cognitive learning cell, which simulate bionic orientation and asymptotic learning ability. Cognitive learning cell is designed on the base of Boltzmann machine and improved Q-Learning algorithm, which executes operant action learning function to approximate the operative part of robot system. The tendency cell adjusts network weights by the use of information entropy to evaluate the function of operate action. The results of the simulation experiment in mobile robot showed that the designed autonomous path-planning strategy lets the robot realize autonomous navigation path planning. The robot learns to select autonomously according to the bionic orientate action and have fast convergence rate and higher adaptability.

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