Research on A Global Path-Planning Algorithm for Unmanned Arial Vehicle Swarm in Three-Dimensional Space Based on Theta–Artificial Potential Field Method

The current challenge in drone swarm technology is three-dimensional path planning and adaptive formation changes. The traditional A* algorithm has limitations, such as low efficiency, difficulty in handling obstacles, and numerous turning points, which make it unsuitable for complex three-dimension...

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Veröffentlicht in:Drones (Basel) Jg. 8; H. 4; S. 125
Hauptverfasser: Zhao, Wen, Li, Liqiao, Wang, Yingqi, Zhan, Hanwen, Fu, Yiqi, Song, Yunfei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.04.2024
Schlagworte:
Algorithms
APF
Barriers
Control algorithms
Control systems
Drone aircraft
Drones
Formation flying
Heuristic
obstacle avoidance
Optimization
Path planning
Planning
Potential fields
Swarm intelligence
Theta global path planning
UAVs cluster
Unmanned aerial vehicles
China
ISSN:2504-446X, 2504-446X
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Abstract The current challenge in drone swarm technology is three-dimensional path planning and adaptive formation changes. The traditional A* algorithm has limitations, such as low efficiency, difficulty in handling obstacles, and numerous turning points, which make it unsuitable for complex three-dimensional environments. Additionally, the robustness of drone formations under the leader–follower mode is low, and effectively handling obstacles within the environment is challenging. To address these issues, this study proposes a virtual leader mode for drone formation flight and introduces a new Theta*–APF method for three-dimensional space drone swarm path planning. This algorithm optimizes the A* algorithm by transforming it into an omnidirectional forward Theta* algorithm. It also enhances the heuristic function by incorporating artificial potential field methods in a three-dimensional environment. Formation organization and control of UAVs is achieved using speed-control modes. Compared to the conventional A* algorithm, the Theta*–APF algorithm reduces the search time by about 60% and the trip length by 10%, in addition to the safer flight of the UAV formation, which is subject to artificial potential field repulsion by about 42%.
AbstractList The current challenge in drone swarm technology is three-dimensional path planning and adaptive formation changes. The traditional A* algorithm has limitations, such as low efficiency, difficulty in handling obstacles, and numerous turning points, which make it unsuitable for complex three-dimensional environments. Additionally, the robustness of drone formations under the leader–follower mode is low, and effectively handling obstacles within the environment is challenging. To address these issues, this study proposes a virtual leader mode for drone formation flight and introduces a new Theta*–APF method for three-dimensional space drone swarm path planning. This algorithm optimizes the A* algorithm by transforming it into an omnidirectional forward Theta* algorithm. It also enhances the heuristic function by incorporating artificial potential field methods in a three-dimensional environment. Formation organization and control of UAVs is achieved using speed-control modes. Compared to the conventional A* algorithm, the Theta*–APF algorithm reduces the search time by about 60% and the trip length by 10%, in addition to the safer flight of the UAV formation, which is subject to artificial potential field repulsion by about 42%.
Audience Academic
Author Zhan, Hanwen
Song, Yunfei
Li, Liqiao
Zhao, Wen
Wang, Yingqi
Fu, Yiqi
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  surname: Song
  fullname: Song, Yunfei
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Snippet The current challenge in drone swarm technology is three-dimensional path planning and adaptive formation changes. The traditional A* algorithm has...
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StartPage 125
SubjectTerms Algorithms
APF
Barriers
Control algorithms
Control systems
Drone aircraft
Drones
Formation flying
Heuristic
obstacle avoidance
Optimization
Path planning
Planning
Potential fields
Swarm intelligence
Theta global path planning
UAVs cluster
Unmanned aerial vehicles
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Title Research on A Global Path-Planning Algorithm for Unmanned Arial Vehicle Swarm in Three-Dimensional Space Based on Theta–Artificial Potential Field Method
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