Path Planning of Unmanned Surface Vehicle Integrating Optimized A* and Dynamic Window Approach

Abstract

There are problems such as poor real-time performance, low efficiency of path planning, and easy to fall into local optimum when using traditional path planning algorithms for unmanned surface vehicle path planning. Aiming at the above problems, this paper proposes a path planning algorithm for unmanned surface vehicles that integrates the optimized A* algorithm and the dynamic window approach. The algorithm firstly optimizes and adjusts the cost function and path search method of the traditional A* algorithm, and secondly adopts the double broken line optimization strategy to greatly reduce the number of path inflection points and improve the smoothness of the global path. Finally, by introducing the path evaluation sub-function into the evaluation function of the dynamic window approach, the optimized A* algorithm is integrated with the dynamic window approach. The simulation results show that the path search efficiency of the algorithm in the static environment is significantly improved compared with the traditional A* algorithm, the smoothness of the path is better than that of the traditional A* algorithm, and it has a good dynamic obstacle avoidance effect in the dynamic environment.