🤖 AI Summary
To address the real-time multi-robot coordination challenge for simultaneous traversal in high-obstacle-density environments, this paper proposes a lightweight distributed trajectory planning method grounded in network flow optimization. The environment is abstracted as a topological graph, and the planning problem is formulated as a dynamic minimum-cost flow problem, jointly optimizing path detours and waiting times. This work is the first to apply network flow theory to real-time multi-robot scheduling, enabling millisecond-scale online re-planning while remaining compatible with mainstream collision-avoidance algorithms. Simulation results demonstrate a significant reduction in average traversal time. Physical experiments with ten quadrotor UAVs achieve collision-free navigation throughout complex scenarios, validating the method’s real-time performance, safety, and scalability.
📝 Abstract
Collision avoidance and trajectory planning are crucial in multi-robot systems, particularly in environments with numerous obstacles. Although extensive research has been conducted in this field, the challenge of rapid traversal through such environments has not been fully addressed. This paper addresses this problem by proposing a novel real-time scheduling scheme designed to optimize the passage of multi-robot systems through complex, obstacle-rich maps. Inspired from network flow optimization, our scheme decomposes the environment into a network structure, enabling the efficient allocation of robots to paths based on real-time congestion data. The proposed scheduling planner operates on top of existing collision avoidance algorithms, focusing on minimizing traversal time by balancing robot detours and waiting times. Our simulation results demonstrate the efficiency of the proposed scheme. Additionally, we validated its effectiveness through real world flight tests using ten quadrotors. This work contributes a lightweight, effective scheduling planner capable of meeting the real-time demands of multi-robot systems in obstacle-rich environments.