Formation control for UAVs using a Flux Guided approach

Hartley, John, Shum, Hubert, Ho, Edmond, Wang, He and Ramamoorthy, Subramanian (2022) Formation control for UAVs using a Flux Guided approach. Expert Systems with Applications, 205. p. 117665. ISSN 0957-4174

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Official URL: https://doi.org/10.1016/j.eswa.2022.117665

Abstract

Existing studies on formation control for unmanned aerial vehicles (UAV) have not considered encircling targets where an optimum coverage of the target is required at all times. Such coverage plays a critical role in many real-world applications such as tracking hostile UAVs. This paper proposes a new path planning approach called the Flux Guided (FG) method, which generates collision-free trajectories for multiple UAVs while maximising the coverage of target(s). Our method enables UAVs to track directly toward a target whilst maintaining maximum coverage. Furthermore, multiple scattered targets can be tracked by scaling the formation during flight. FG is highly scalable since it only requires communication between sub-set of UAVs on the open boundary of the formation's surface. Experimental results further validate that FG generates UAV trajectories 1.5× shorter than previous work and that trajectory planning for 9 leader/follower UAVs to surround a target in two different scenarios only requires 0.52 s and 0.88 s, respectively. The resulting trajectories are suitable for robotic controls after time-optimal parameterisation; we demonstrate this using a 3d dynamic particle system that tracks the desired trajectories using a PID controller.

Item Type: Article
Additional Information: Funding Information: This work is supported by the MOD Chief Scientific Adviser’s Research Programme, through the Defence and Security Accelerator, UK (Ref: DSTLX-1000140725 ), and the Royal Society, UK (Ref: IESR2181024 and IESR1191147 ).
Uncontrolled Keywords: Artificial harmonic field, Electric flux, Formation encirclement, Multi-agent motion planning, Unmanned aerial vehicles
Subjects: G400 Computer Science
G700 Artificial Intelligence
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Rachel Branson
Date Deposited: 14 Jul 2022 14:23
Last Modified: 14 Jul 2022 14:30
URI: http://nrl.northumbria.ac.uk/id/eprint/49550

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