Xiao Wu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Xiao Wu has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Jing Yang
Department of Science, Taiyuan Institute of Technology, Taiyuan 030008, China
1 shared publication - Jing Zhao
Department of Mechanical Engineering, Taiyuan Institute of Technology, Taiyuan 030008, China
1 shared publication - Xiaoling Meng
Department of Mechanical Engineering, Taiyuan Institute of Technology, Taiyuan 030008, China
1 shared publication - Xiaoqiang Zhang
Department of Mechanical Engineering, Taiyuan Institute of Technology, Taiyuan 030008, China
1 shared publication - Xijing Zhu
School of Mechanical Engineering, North University of China, Taiyuan 030051, China
1 shared publication - Zhikang Zhang
China Railway 12th Bureau Group Third Engineering Co., Ltd., Taiyuan 030027, China
1 shared publication
Latest Publications
- Open Access
Obstacle Avoidance Path Planning for Unmanned Aerial Vehicle in Workshops Based on Parameter-Optimized Artificial Potential Field A* Algorithm
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.
Open AccessObstacle Avoidance Path Planning for Unmanned Aerial Vehicle in Workshops Based on Parameter-Optimized Artificial Potential Field A* Algorithm
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.