Path Planning and Obstacle Avoidance Technology for Mobile Robots using Improved A-Star Algorithm

Abstract

Path planning is a key element for robots to move from their own position to their target position. Currently, the A-star algorithm is widely used for route mapping. An enhanced version of the A-star algorithm has been introduced to address the inherent problems of long routes and duplicate route points in traditional A-star methods. Firstly, expand the search neighborhood from 4 to 16. Secondly, eliminate redundant nodes, improve the evaluation function, and ultimately achieve path smoothing. This article introduces a two-layer expansion method aimed at solving the difficulty of navigation in narrow spaces during route surveys. By improving the amplification parameters of obstacles and improving the evaluation mechanism, autonomous vehicle can successfully reach their destinations. A simulation experiment was conducted using MATLAB. The results show that the improved algorithm can reduce redundant nodes, shorten path length, and achieve path optimization.