Research on Path Planning of Mobile Robot with Improved SARSA Algorithm
Aiming at the problems of slow convergence speed,high exploration randomness,and poor path planning performance in mobile ro-bot path planning using traditional SARSA algorithm,this paper proposes an improved SARSA algorithm that combines artificial potential field method with traditional SARSA algorithm.Firstly,the reward function of the algorithm is controlled by the gravity function of the artificial po-tential field to increase the guidance during exploration;Secondly,using the repulsive function of the artificial potential field to generate μ To effectively adjust the Q value in path planning,the value should be adjusted to lower the Q value as it approaches obstacles,thereby improv-ing the convergence speed of the path planning algorithm and reducing the frequency of collisions with obstacles.The improved SARSA algo-rithm was compared with other algorithms through simulation experiments,and the results showed that the improved SARSA algorithm has sig-nificantly improved performance in convergence speed,average learning time,average learning steps,and average number of collisions with obstacles,which can effectively enhance the path planning ability of intelligent actions of mobile robots.
reinforcement learningimproved SARSAartificial potential field methodpath planningmobile robot
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