Global Path Planning Method by Fusion of A-star Algorithm and Sparrow Search Algorithm

Abstract

A global path planning method (A-SSA) that integrates the A-star algorithm and Sparrow Search Algorithm (SSA) is proposed for the shortest path planning problem of Automated Guided Vehicles (AGV) in a static raster environment. The first stage of the method uses the sparrow algorithm to obtain several key grid points in the raster map, and then uses the A-star algorithm to connect these grid points; the second stage uses the ray method to remove the redundant nodes, and then uses the Bessel curve to generate a continuous, collision-free and smooth shortest path after obtaining the simplified vital nodes. The back-off mechanism is studied for the deadlock problem in path planning, simulation experiments are conducted for the three algorithms within a 30x30 raster map with obstacle coverage of 20%, 25%, 30%, 35%, and 40%, respectively, and the experimental results show that the path length planned by the A-SSA method is the shortest, which proves the effectiveness of the method and can provide a The experimental results show that the path length of the A-SSA method is the shortest, which demonstrates the effectiveness of the method and can provide some reference for the shortest path planning of AGV.