Version 1
: Received: 16 July 2024 / Approved: 16 July 2024 / Online: 16 July 2024 (10:40:49 CEST)
How to cite:
Gao, J.; Pan, W. Research, Analysis and Improvement of Unmanned Aerial Vehicle Path Planning Algorithms in Urban Ultra-Low Altitude Airspace. Preprints2024, 2024071321. https://doi.org/10.20944/preprints202407.1321.v1
Gao, J.; Pan, W. Research, Analysis and Improvement of Unmanned Aerial Vehicle Path Planning Algorithms in Urban Ultra-Low Altitude Airspace. Preprints 2024, 2024071321. https://doi.org/10.20944/preprints202407.1321.v1
Gao, J.; Pan, W. Research, Analysis and Improvement of Unmanned Aerial Vehicle Path Planning Algorithms in Urban Ultra-Low Altitude Airspace. Preprints2024, 2024071321. https://doi.org/10.20944/preprints202407.1321.v1
APA Style
Gao, J., & Pan, W. (2024). Research, Analysis and Improvement of Unmanned Aerial Vehicle Path Planning Algorithms in Urban Ultra-Low Altitude Airspace. Preprints. https://doi.org/10.20944/preprints202407.1321.v1
Chicago/Turabian Style
Gao, J. and Weijun Pan. 2024 "Research, Analysis and Improvement of Unmanned Aerial Vehicle Path Planning Algorithms in Urban Ultra-Low Altitude Airspace" Preprints. https://doi.org/10.20944/preprints202407.1321.v1
Abstract
Urban ultra-low altitude airspace (ULAA) presents unique challenges for unmanned aerial vehicle (UAV) path planning due to high building density and regulatory constraints. This study analyzes and improves classical path planning algorithms for UAVs in ULAA. Experiments were conducted using A*, RRT, RRT*, and artificial potential field (APF) methods in a simulated environment based on building data from Chengdu City, China. Results show that traditional algorithms struggle in dense obstacle environments, particularly APF due to local minima issues. Enhancements were proposed: a density-aware heuristic for A*, random perturbation for APF, and a hybrid optimization strategy for RRT*. These modifications improved computation time, path length, and obstacle avoidance. The study provides insights into the limitations of classical algorithms and suggests enhancements for more effective UAV path planning in urban environments.
Engineering, Transportation Science and Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
