让多机器人像水流一样高效穿越障碍区，实时调度实现快速协同通行。
From Edge to Edge: A Flow-Inspired Scheduling Planner for Multi-Robot Systems
- 基于网络流思想构建路径分配模型，动态响应拥堵情况。
- 仿真与十架无人机实测验证，整体通行时间显著降低。
- 适合复杂环境下的多机协同任务，如救援、巡检等场景。
轨迹规划在多机器人系统中至关重要，尤其在障碍物密集环境中。尽管该领域研究广泛，但如何协调多机器人从地图一端高效、连续地穿越至另一端（如穿越任务）仍关注不足。本文针对这一方向性穿越场景，提出一种实时调度方案，使多机器人系统能像水流般从边缘到边缘流畅移动。受网络流优化启发，该方案将环境建模为基于流的网络结构，根据实时拥堵程度动态分配机器人路径。所提调度规划器可集成于现有避障算法之上，通过平衡绕行与等待时间，最小化整体通行时长。仿真结果证明该方案在实现快速协同穿越方面的有效性；此外，十架无人机的真实飞行测试进一步验证了其实际可行性。本工作贡献了一个面向复杂障碍环境中的方向性多机器人穿越任务的流启发式实时调度规划器。代码开源：https://github.com/chengji253/FlowPlanner
原文摘要 · Abstract (English)
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
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