ESCAPE: an efficient and safe distributed UAV swarm exploration framework with collision avoidance perception
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| Vydáno v: | Autonomous Intelligent Systems vol. 5, no. 1 (Dec 2025), p. 30 |
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| Hlavní autor: | |
| Další autoři: | , , , , , , |
| Vydáno: |
Springer Nature B.V.
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| On-line přístup: | Citation/Abstract Full Text Full Text - PDF |
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MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3285903942 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2730-616X | ||
| 024 | 7 | |a 10.1007/s43684-025-00123-y |2 doi | |
| 035 | |a 3285903942 | ||
| 045 | 2 | |b d20251201 |b d20251231 | |
| 100 | 1 | |a Bao, Yaoyang |u Tongji University, School of Computer Science and Technology, Shanghai, China (GRID:grid.24516.34) (ISNI:0000 0001 2370 4535) | |
| 245 | 1 | |a ESCAPE: an efficient and safe distributed UAV swarm exploration framework with collision avoidance perception | |
| 260 | |b Springer Nature B.V. |c Dec 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Significant progress has been made in distributed unmanned aerial vehicle (UAV) swarm exploration. In complex scenarios, existing methods typically rely on shared trajectory information for collision avoidance, but communication timeliness issues may result in outdated trajectories being referenced when making collision avoidance decisions, preventing timely responses to the motion changes of other UAVs, thus elevating the collision risk. To address this issue, this paper proposes a new distributed UAV swarm exploration framework. First, we introduce an improved global exploration strategy that combines the exploration task requirements with the surrounding obstacle distribution to plan an efficient and safe coverage path. Secondly, we design a collision risk prediction method based on relative distance and relative velocity, which effectively assists UAVs in making timely collision avoidance decisions. Lastly, we propose a multi-objective local trajectory optimization function that considers the positions of UAVs and static obstacles, thereby planning safe flight trajectories. Extensive simulations and real-world experiments demonstrate that this framework enables safe and efficient exploration in complex environments. | |
| 653 | |a Velocity | ||
| 653 | |a Collaboration | ||
| 653 | |a Trajectory optimization | ||
| 653 | |a Cooperation | ||
| 653 | |a Communication | ||
| 653 | |a Unmanned aerial vehicles | ||
| 653 | |a Optimization | ||
| 653 | |a Sensors | ||
| 653 | |a Decision making | ||
| 653 | |a Robots | ||
| 653 | |a Collisions | ||
| 653 | |a Collision avoidance | ||
| 653 | |a Methods | ||
| 653 | |a Multiple objective analysis | ||
| 653 | |a Traveling salesman problem | ||
| 653 | |a Trajectory planning | ||
| 653 | |a Barriers | ||
| 653 | |a Decisions | ||
| 653 | |a Efficiency | ||
| 700 | 1 | |a Du, Siyuan |u Tongji University, School of Computer Science and Technology, Shanghai, China (GRID:grid.24516.34) (ISNI:0000 0001 2370 4535) | |
| 700 | 1 | |a Jiang, Qingwei |u Tongji University, School of Computer Science and Technology, Shanghai, China (GRID:grid.24516.34) (ISNI:0000 0001 2370 4535) | |
| 700 | 1 | |a Li, Yixuan |u Tongji University, School of Computer Science and Technology, Shanghai, China (GRID:grid.24516.34) (ISNI:0000 0001 2370 4535) | |
| 700 | 1 | |a Zhao, Bochao |u Tianjin University, State Key Laboratory of Smart Power Distribution Equipment and System, School of Electrical and Information Engineering, Tianjin, China (GRID:grid.33763.32) (ISNI:0000 0004 1761 2484) | |
| 700 | 1 | |a Wang, Gang |u Beijing Institute of Technology, State Key Laboratory of Autonomous Intelligent Unmanned Systems, School of Automation, Beijing, China (GRID:grid.43555.32) (ISNI:0000 0000 8841 6246) | |
| 700 | 1 | |a Liu, Qingwen |u Tongji University, School of Computer Science and Technology, Shanghai, China (GRID:grid.24516.34) (ISNI:0000 0001 2370 4535) | |
| 700 | 1 | |a Xiong, Mingliang |u Tongji University, School of Computer Science and Technology, Shanghai, China (GRID:grid.24516.34) (ISNI:0000 0001 2370 4535) | |
| 773 | 0 | |t Autonomous Intelligent Systems |g vol. 5, no. 1 (Dec 2025), p. 30 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3285903942/abstract/embedded/Q8Z64E4HU3OH5N8U?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3285903942/fulltext/embedded/Q8Z64E4HU3OH5N8U?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3285903942/fulltextPDF/embedded/Q8Z64E4HU3OH5N8U?source=fedsrch |