RRT*-Based Leader-Follower Trajectory Planning and Tracking in Multi-Agent Systems

Agachi, Catalina; Arvin, Farshad; Hu, Junyan

Abstract

Coordination of multi-agent systems has received significant attention during the past few years owing to its wide real-world applications, such as cooperative exploration, aircraft formation, and autonomous vehicle platooning. To address this issue, this research presents a novel method for multi-agent systems to navigate through environments with obstacles. The system consists of a group of agents with a leader-follower structure, where the leader aids in guiding the agents toward the target location and the followers are steered to maintain a flexible formation. To achieve cooperation, the agents communicate within a connected and undirected network, exchanging information within a specific radius. The leader's path is generated using the RRT* algorithm, which serves as a reference for the followers. A control law utilizes consensus and APF is then implemented, ensuring coordinated motion while maintaining safe distances among agents and between agents and obstacles. Finally, the effectiveness of the developed two-layer coordination strategy is verified by simulations.

Citation

Agachi, C., Arvin, F., & Hu, J. (2024, August). RRT*-Based Leader-Follower Trajectory Planning and Tracking in Multi-Agent Systems. Presented at 2024 IEEE International Conference on Intelligent Systems (IS), Varna, Bulgaria

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2024 IEEE International Conference on Intelligent Systems (IS)
• Start Date: Aug 29, 2024
• End Date: Aug 31, 2024
• Acceptance Date: Jun 11, 2024
• Online Publication Date: Oct 9, 2024
• Publication Date: Oct 9, 2024
• Deposit Date: Aug 9, 2024
• Publicly Available Date: Oct 10, 2024
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Pages: 1-6
• Series ISSN: 2832-4145
• Book Title: 2024 IEEE International Conference on Intelligent Systems (IS)
• DOI: https://doi.org/10.1109/IS61756.2024.10705259
• Keywords: Path planning; cooperative control; multi-robot systems; swarm robotics
• Public URL: https://durham-repository.worktribe.com/output/2745359