From Edge to Edge: A Flow-Inspired Scheduling Planner for Multi-Robot Systems

TL;DR

This paper introduces a flow-inspired, real-time scheduling planner for multi-robot systems to efficiently traverse obstacle-rich environments from edge to edge, improving coordination and reducing traversal time.

Contribution

It presents a novel scheduling scheme based on network flow optimization that integrates with collision avoidance to enhance multi-robot traversal efficiency.

Findings

Simulation results show faster, more coordinated traversal.

Real-world drone tests validate practical feasibility.

The scheme effectively balances detours and waiting times.

Abstract

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