Obstacle Avoidance Path Planning for Unmanned Ships Based on Improved DDPG
WU Yifan
LI Zhen
WANG Nan
Abstract：In response to the problems of long training time,slow convergence,and poor performance in some complex situa-tions in deep reinforcement learning in dynamic environments,a combination of multi-step bootstrap,perturbation fluid algorithm(IFDS),and deep deterministic policy gradient algorithm(DDPG)is proposed.Firstly,N-step Bootstrap is added to DDPG to en-dow the model with the ability to combine multiple future time steps.Secondly,the perturbation fluid algorithm is introduced to joint-ly construct a potential field with the velocity information of obstacles in the environment,solving the problem of high-dimensional continuous action space and improving training efficiency.Finally,an environment consisting of a single obstacle and multiple obsta-cles is constructed to simulate and validate the algorithm.The simulation results show that the improved DDPG algorithm has higher training stability and speed compared to traditional DDPG algorithms in the simulation environment,and it can successfully achieve dynamic obstacle avoidance in more complex environments.At the same time,the success rate of training is improved.
Keywords：deep deterministic strategy gradient algorithmmulti-step bootstrappingperturbation fluid algorithmpath planningunmanned ship
Publication Date：2025-12-20
Online Publishing Date：2026-03-23(First online date of this platform, not the publication date of the document)
Pages：5( 36-40 )