T-STAR: Time-Optimal Swarm Trajectory Planning for Quadrotor Unmanned Aerial Vehicles

Pan, Honghao; Zahmatkesh, Mohsen; Rekabi-Bana, Fatemeh; Arvin, Farshad; Hu, Junyan

Abstract

This paper introduces a time-optimal swarm tra-jectory planner for cooperative unmanned aerial vehicle (UAV) systems, designed to generate collision-free trajectories for flocking control in cluttered environments. To achieve this goal, model predictive contour control is utilised to generate time-optimal tra-jectories for each UAV. By demonstrating the differential flatness dynamic equations, the system state constraints are simplified, the algorithm's complexity is reduced, and the overall stability is improved. Additionally, flocking control is achieved among multiple UAVs by applying virtual repulsive and attractive forces. Furthermore, an event-triggered trajectory deconflict strategy for trajectory replanning is considered to resolve multiple trajectory conflicts. Comparative experiments with baseline methods have confirmed that the proposed planner can generate faster and safer trajectories than conventional methods.

Citation

Pan, H., Zahmatkesh, M., Rekabi-Bana, F., Arvin, F., & Hu, J. (in press). T-STAR: Time-Optimal Swarm Trajectory Planning for Quadrotor Unmanned Aerial Vehicles. IEEE Transactions on Intelligent Transportation Systems,

• Journal Article Type: Article
• Acceptance Date: Mar 22, 2025
• Deposit Date: Apr 2, 2025
• Journal: IEEE Transactions on Intelligent Transportation Systems
• Print ISSN: 1524-9050
• Electronic ISSN: 1558-0016
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Keywords: Index Terms-Unmanned aerial vehicles; optimisation; model predictive contour control; distributed trajectory planning; colli- sion avoidance; swarm robotics
• Public URL: https://durham-repository.worktribe.com/output/3772764