An Improved DDQN Path Planning Algorithm for Unmanned Vehicle
Aiming at the problems of slow convergence speed and low path quality in the DDQN algo-rithm,a path planning algorithm for unmanned vehicles based on DDQN was studied.First,the reward val-ue information is made full use of by obtaining the reward values at a plurality of times,accumulating the rewards,and averaging the rewards.Then,the artificial potential field method is improved by optimizing the direction of repulsion generation,and the improved artificial potential field method is used to replace the random exploration to improve the convergence rate.Finally,the redundant nodes are removed by judging the relationship between the path and obstacles,and the Bessel curve is used to smooth the path to improve the quality of the path.Simulation results show that the convergence rate of the improved DDQN algorithm is improved by 69.01%and 55.88%,and the path length is shortened by 21.39%and 14.33%,respec-tively,compared with the original DDQN in the two environments of 20×20,and the path smoothness is higher.The improved DDQN algorithm is deployed on the unmanned vehicle to test,and the results show that the unmanned vehicle can complete the path planning task well.
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