Research on ship path planning method based on deep reinforcement learning
The existing path planning algorithm needs a large amount of prior information in the face of complex environment,and has problems such as large amount of computation,excessive transition and poor search accuracy.The use of deep reinforcement learning algorithm can make up the above defects,but the algorithm itself convergence is slow and other problems.To solve this problem,an improved Artificial Po-tential Field method(APF)is proposed to optimize the reward function of the deep reinforcement learning algorithm,and the path is smoothed by Bessel curve.Finally,a relatively smooth sailing path is output.Under the same environment,the effect of the improved algorithm model and the existing method of path planning is compared and analyzed.The results show that the DQN-APF algorithm has improved the ability of the generated path length,smoothness,planning completion time and other ship path comprehensive planning parameters.
万方数据
维普
