Path planning strategy of snake-like robot in complex environment
In order to complete the reconnaissance task in the complex environment of multiple obstacles,improve the path search ability of the snake-like robot and enhance the autonomous decision-making ability,this paper proposes a path planning control strategy based on prior knowledge inference library reinforcement learning.Firstly,the motion model and interactive environment model of snake-like robot are established.Secondly,by establishing an action hierarchical selection model composed of Fuzzy logic prior knowledge inference system(FLIS)and Soft Actor-Critic(SAC)action network,the output action space is discretized to adjust the motion control accuracy,and a reward and punishment mechanism is provided to guide the robot to interact with the environment model to realize the continuous generation process of robot motion autonomous decision-making.The simulation results show that the convergence speed and robustness of the motion control strategy obtained by the improved algorithm proposed in the multi-obstacle environment are significantly improved,the number of training explorations is reduced,and the adaptability of the robot to the complex environment is improved.The experiment verifies the feasibility of the strategy model generated by the training in the real environment.
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