Traffic Flow Learning Enhanced Large-Scale Multi-Robot Cooperative Path Planning Under Uncertainties

Robotic systems with hundreds or even thousands of robots are widely implemented in logistic and industrial applications. In such systems, cooperative path planning is of great importance, as local congestion and motion conflict may greatly degrade system performance, especially in the presence of uncertainties. Our idea is to consider traffic flow equilibrium in path planning to relieve any potential congestion and increase efficiency. In this paper, we propose a hierarchical framework, which includes a traffic flow prediction layer, a sector-level planning layer, and a road-level coordination layer. In traffic flow prediction, we propose a spatio-temporal graph neural network that integrates local information to predict the evolution of future robot density distribution. In sector-level planning, we generate sector-level paths that consider travel distance and traffic flow equilibrium simultaneously. In road-level coordination, we implement the conflict-based search algorithm within each sector to ensure conflict-free local paths. In addition, we also explicitly consider motion/communication uncertainties that are unavoided in practical systems. We validate our effectiveness in simulations with over 1000 robots, what’s more, real experiments are provided.