Liqiao Li
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Liqiao Li has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Hanwen Zhan
School of Civil Aviation, Northwestern Polytechnical University, Xian, Shaanxi, China
1 shared publication - Wen Zhao
State Key Laboratory for Fine Exploration and Intelligent Development of Coal Resources, China University of Mining & Technology, Beijing, China
1 shared publication - Yingqi Wang
School of Civil Aviation, Northwestern Polytechnical University, Xian, Shaanxi, China
1 shared publication - Yiqi Fu
School of Civil Aviation, Northwestern Polytechnical University, Xian, Shaanxi, China
1 shared publication - Yunfei Song
School of Civil Aviation, Northwestern Polytechnical University, Xi’an 710072, China
1 shared publication
Latest Publications
- Open Access
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-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%.
Open AccessResearch 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-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%.