Trajectory planning is crucial in multi-robot systems, particularly in environments with numerous obstacles. While extensive research has been conducted in this field, the challenge of coordinating multiple robots to flow collectively from one side of the map to the other-such as in crossing missions through obstacle-rich spaces-has received limited attention. This paper focuses on this directional traversal scenario by introducing a real-time scheduling scheme that enables multi-robot systems to move from edge to edge, emulating the smooth and efficient flow of water. Inspired by network flow optimization, our scheme decomposes the environment into a flow-based network structure, enabling the efficient allocation of robots to paths based on real-time congestion levels. The proposed scheduling planner operates on top of existing collision avoidance algorithms, aiming to minimize overall traversal time by balancing detours and waiting times. Simulation results demonstrate the effectiveness of the proposed scheme in achieving fast and coordinated traversal. Furthermore, real-world flight tests with ten drones validate its practical feasibility. This work contributes a flow-inspired, real-time scheduling planner tailored for directional multi-robot traversal in complex, obstacle-rich environments. Code: https://github.com/chengji253/FlowPlanner

翻译：轨迹规划在多机器人系统中至关重要，尤其在存在大量障碍物的环境中。尽管该领域已有广泛研究，但协调多个机器人从地图一侧集体流动至另一侧（例如在充满障碍物的空间中进行穿越任务）的挑战尚未得到充分关注。本文聚焦于此类定向穿越场景，提出一种实时调度方案，使多机器人系统能够像水流般平滑高效地从边缘移动至边缘。受网络流优化的启发，该方案将环境分解为基于流的网络结构，从而根据实时拥堵程度将机器人高效分配至路径。所提出的调度规划器在现有避碰算法之上运行，旨在通过平衡绕行与等待时间来最小化整体穿越时间。仿真结果证明了该方案在实现快速协调穿越方面的有效性。此外，通过十架无人机的实际飞行测试验证了其实际可行性。本研究贡献了一种受流式启发的实时调度规划器，专为复杂多障碍环境中的定向多机器人穿越任务而设计。代码：https://github.com/chengji253/FlowPlanner