An Improved A-Star Algorithm for Path Planning in Robot-Assisted Long Bone Fracture Reduction
Qin Gao, Xiaoyong Wu, Jun Ding, Bo Yuan, Yujin Wang, Ruizhi Shu
Abstract
Long bone fractures are common clinical conditions, yet the development of robot systems for closed reduction surgery remains in its early stages. The key challenge in this field is the lack of an efficient and precise path planning algorithm. To address this issue, this study proposes an improved A-star (A*) algorithm for path planning to enhance the accuracy and efficiency of fracture reduction. The algorithm begins by expanding a random node using the fundamental A* algorithm. An artificial potential field (APF) algorithm is then incorporated to optimize the generation of sample node and enhance obstacle avoidance. Additionally, a cylindrical bounding box method is employed for collision detection, and a B-spline curve is utilized to smooth the generated path. The experimental validation is conducted on a fracture reduction robot system, demonstrating that the optimized path achieves clinically acceptable accuracy, significantly enhancing the precision and reliability of the reduction procedure.
BibTeX
@inproceedings{iros2025_animprovedastara,
title = {An Improved A-Star Algorithm for Path Planning in Robot-Assisted Long Bone Fracture Reduction},
author = {Qin Gao and Xiaoyong Wu and Jun Ding and Bo Yuan and Yujin Wang and Ruizhi Shu},
booktitle = {IROS 2025},
year = {2025}
}