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Fault-tolerant cooperative navigation of networked UAV swarms for forest fire monitoring

Hu, Junyan; Niu, Hanlin; Carrasco, Joaquin; Lennox, Barry; Arvin, Farshad

Authors

Junyan Hu

Hanlin Niu

Joaquin Carrasco

Barry Lennox



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