@article{GAO2025, 
author = {Hui GAO and Yuhong JIA and Liwen XU and Fengxing PAN and Shaowei LI and Yaoming ZHOU},
title = {Anytime algorithm based on adaptive variable-step-size mechanism for path planning of UAVs},
year = {2025},
journal = {Chinese Journal of Aeronautics},
volume = {38},
number = {9},
keywords = {Path planning, Anytime algorithm, Adaptive variable-step-size, Suboptimality bound, Unmanned aerial vehicle (UAV)},
url = {https://www.sciopen.com/article/10.1016/j.cja.2024.09.007},
doi = {10.1016/j.cja.2024.09.007},
abstract = {For autonomous Unmanned Aerial Vehicles (UAVs) flying in real-world scenarios, time for path planning is always limited, which is a challenge known as the anytime problem. Anytime planners address this by finding a collision-free path quickly and then improving it until time runs out, making UAVs more adaptable to different mission scenarios. However, current anytime algorithms based on A* have insufficient control over the suboptimality bounds of paths and tend to lose their anytime properties in environments with large concave obstacles. This paper proposes a novel anytime path planning algorithm, Anytime Radiation A* (ARaA*), which can generate a series of suboptimal paths with improved bounds through decreasing search step sizes and can generate the optimal path when time is sufficient. The ARaA* features two main innovations: an adaptive variable-step-size mechanism and elliptic constraints based on waypoints. The former helps achieve fast path searching in various environments. The latter allows ARaA* to control the suboptimality bounds of paths and further enhance search efficiency. Simulation experiments show that the ARaA* outperforms Anytime Repairing A* (ARA*) and Anytime D* (AD*) in controlling suboptimality bounds and planning time, especially in environments with large concave obstacles. Final flight experiments demonstrate that the paths planned by ARaA* can ensure the safe flight of quadrotors.}
}