Mixed Controller Design for Multi-Vehicle Formation Based on Edge and Bearing Measurements

Wu, Kefan; Hu, Junyan; Lennox, Barry; Arvin, Farshad

Abstract

Inspired by natural swarm collective behaviors such as colonies of bees and schools of fish, coordination strategies in swarm robotics have received significant attention in recent years. In this paper, a mixed formation control design based on edge and bearing measurements is proposed for networked multi-vehicle systems. Although conventional edge-based controllers have been widely used in many formation tasks, the tracking accuracy may not be guaranteed in some extreme environments as it depends on the quality of the sensors and requires the exact position data of each vehicle. To overcome this limitation, we combine the edge-based controller with a bearing-based method where only relative bearings among the vehicles are required. Depending on the sensing-ability of the robotic platform, this mixed control method can provide an efficient solution to maximise the tracking performance. Both leaderless and leader-follower cases are considered in the protocol design. The stability of the networked multi-vehicle systems under the proposed mixed formation approach is ensured by Lyapunov theory. Finally, we present simulation results to verify the effectiveness of the theoretical results.

Citation

Wu, K., Hu, J., Lennox, B., & Arvin, F. (2022). Mixed Controller Design for Multi-Vehicle Formation Based on Edge and Bearing Measurements. In 2022 European Control Conference (ECC). https://doi.org/10.23919/ecc55457.2022.9838436

• Conference Name: 2022 European Control Conference (ECC)
• Conference Location: London, United Kingdom
• Start Date: Jul 12, 2022
• End Date: Jul 15, 2022
• Acceptance Date: Feb 28, 2022
• Online Publication Date: Aug 5, 2022
• Publication Date: 2022
• Deposit Date: Nov 7, 2023
• Publicly Available Date: Nov 7, 2023
• Publisher: Institute of Electrical and Electronics Engineers
• Book Title: 2022 European Control Conference (ECC)
• ISBN: 9781665497336
• DOI: https://doi.org/10.23919/ecc55457.2022.9838436
• Public URL: https://durham-repository.worktribe.com/output/1899475