Junyan Hu
Fault-tolerant cooperative navigation of networked UAV swarms for forest fire monitoring
Hu, Junyan; Niu, Hanlin; Carrasco, Joaquin; Lennox, Barry; Arvin, Farshad
Authors
Hanlin Niu
Joaquin Carrasco
Barry Lennox
Professor Farshad Arvin farshad.arvin@durham.ac.uk
Professor
Abstract
Coordination of unmanned aerial vehicle (UAV) swarms has received significant attention due to its wide practical applications including search and rescue, cooperative exploration and target surveillance. Motivated by the flexibility of the UAVs and the recent advancement of graph-based cooperative control strategies, this paper aims to develop a fault-tolerant cooperation framework for networked UAVs with applications to forest fire monitoring. Firstly, a cooperative navigation strategy based on network graph theory is proposed to coordinate all the connected UAVs in a swarm in the presence of unknown disturbances. The stability of the aerial swarm system is guaranteed using the Lyapunov approach. In case of damage to the actuators of some of the UAVs during the mission, a decentralized task reassignment algorithm is then applied, which makes the UAV swarm more robust to uncertainties. Finally, a novel geometry-based collision avoidance approach using onboard sensory information is proposed to avoid potential collisions during the mission. The effectiveness and feasibility of the proposed framework are verified initially by simulations and then using real-world flight tests in outdoor environments.
Citation
Hu, J., Niu, H., Carrasco, J., Lennox, B., & Arvin, F. (2022). Fault-tolerant cooperative navigation of networked UAV swarms for forest fire monitoring. Aerospace Science and Technology, 123, Article 107494. https://doi.org/10.1016/j.ast.2022.107494
Journal Article Type | Article |
---|---|
Acceptance Date | Mar 7, 2022 |
Online Publication Date | Mar 18, 2022 |
Publication Date | 2022-04 |
Deposit Date | May 27, 2022 |
Journal | Aerospace Science and Technology |
Print ISSN | 1270-9638 |
Publisher | Elsevier |
Volume | 123 |
Article Number | 107494 |
DOI | https://doi.org/10.1016/j.ast.2022.107494 |
Public URL | https://durham-repository.worktribe.com/output/1205475 |
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