Robust UAV Path Planning with Obstacle Avoidance for Emergency Rescue

The unmanned aerial vehicles (UAVs) are efficient tools for diverse tasks such as electronic reconnaissance, agricultural operations and disaster relief. In the complex three-dimensional (3D) environments, the path planning with obstacle avoidance for UAVs is a significant issue for security assurance. In this paper, we construct a comprehensive 3D scenario with obstacles and no-fly zones for dynamic UAV trajectory. Moreover, a novel artificial potential field algorithm coupled with simulated annealing (APF-SA) is proposed to tackle the robust path planning problem. APF-SA modifies the attractive and repulsive potential functions and leverages simulated annealing to escape local minimum and converge to globally optimal solutions. Simulation results demonstrate that the effectiveness of APFSA, enabling efficient autonomous path planning for UAVs with obstacle avoidance.

Authors

Institutions

Publication Details

Published
2025-03-24
DOI
https://doi.org/10.1109/wcnc61545.2025.10978825
Citations
5
Primary Topic
Robotic Path Planning Algorithms
Type
article
Field-Weighted Citation Impact
6.38

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Robust UAV Path Planning with Obstacle Avoidance for Emergency Rescue

Hanzhi Gu, Hongda Shi, Lijun He, Qihui Wu et al.
5 citations
Robotic Path Planning Algorithms
6.38
article

Robust UAV Path Planning with Obstacle Avoidance for Emergency Rescue

Hanzhi Gu, Hongda Shi, Lijun He, Qihui Wu, Ziye Jia, Chenyu Shi, Jilei Mao
article en
5 citations

Abstract

The unmanned aerial vehicles (UAVs) are efficient tools for diverse tasks such as electronic reconnaissance, agricultural operations and disaster relief. In the complex three-dimensional (3D) environments, the path planning with obstacle avoidance for UAVs is a significant issue for security assurance. In this paper, we construct a comprehensive 3D scenario with obstacles and no-fly zones for dynamic UAV trajectory. Moreover, a novel artificial potential field algorithm coupled with simulated annealing (APF-SA) is proposed to tackle the robust path planning problem. APF-SA modifies the attractive and repulsive potential functions and leverages simulated annealing to escape local minimum and converge to globally optimal solutions. Simulation results demonstrate that the effectiveness of APFSA, enabling efficient autonomous path planning for UAVs with obstacle avoidance.

China University of Mining and Technology (CN), Ministry of Industry and Information Technology (CN), Nanjing University of Aeronautics and Astronautics (CN)
National Natural Science Foundation of China, Aeronautical Science Foundation of China, National Mobile Communications Research Laboratory, Southeast University
Climate action
Openalex Percentile: Top 4%
Robotic Path Planning Algorithms
6.38
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.