Obstacle Avoidance Path Planning for Unmanned Aerial Vehicle in Workshops Based on Parameter-Optimized Artificial Potential Field A* Algorithm

Abstract

As the intelligent transformation of manufacturing accelerates, Unmanned Aerial Vehicles are increasingly being deployed for workshop operations, making efficient obstacle avoidance path planning a critical requirement. This paper introduces a parameter-optimized path planning method for the Unmanned Aerial Vehicle, termed the Artificial Potential Field A* algorithm, which enhances the standard A* approach through the integration of an artificial potential field and a variable step size strategy. The variable step size mechanism allows dynamic adjustment of the search step size, while potential field values from the artificial potential field are embedded into the cost function to improve planning accuracy. Key parameters of the hybrid algorithm are subsequently optimized using response surface methodology, with a regression model built to analyze parameter interactions and determine the optimal configuration. Simulation results across multiple performance indicators confirm that the proposed Artificial Potential Field A* algorithm delivers superior outcomes in path length, attitude angle variation, and flight altitude stability. This approach provides an effective solution for enhancing Unmanned Aerial Vehicle operational efficiency in production workshops.