Path Planning of Parking Robot Based on Improved D3QN Algorithm
The parking robot emerges as a solution to the urban parking problem,and its path planning is an important research direction.Due to the limitations of the A*algorithm,the deep reinforcement learning idea is introduced in this article,and im-proves the D3QN algorithm.Through replacing the convolutional network with a residual network and introducing attention mechanisms,the SE-RD3QN algorithm is proposed to improve network degradation and convergence speed,and enhance model accuracy.During the algorithm training process,the reward and punishment mechanism is improved to achieve rapid conver-gence of the optimal solution.Through comparing the experimental results of the D3QN algorithm and the RD3QN algorithm with added residual layers,it shows that the SE-RD3QN algorithm achieves faster convergence during model training.Compared with the currently used A*+TEB algorithm,SE-RD3QN can obtain shorter path length and planning time in path planning.Finally,the effectiveness of the algorithm is further verified through physical experiments simulating a car,which provides a new solution for parking path planning.
deep reinforcement learningparking robotpath planninglidar sensors
国家科技期刊平台
NSTL
万方数据
