CGLR: Dense Multi-Agent Navigation Using Voronoi Cells and Congestion Metric-based Replanning


We present a decentralized path-planning algorithm for navigating multiple differential-drive robots in dense environments. In contrast to prior decentralized methods, we propose a novel congestion metric-based replanning that couples local and global planning techniques to efficiently navigate in scenarios with multiple corridors. To handle dense scenes with narrow passages, our approach computes the initial path for each agent to its assigned goal using a lattice planner. Based on neighbors’ information, each agent performs online replanning using a congestion metric that tends to reduce the collisions and improves the navigation performance. Furthermore, we use the Voronoi cells of each agent to plan the local motion as well as a corridor selection strategy to limit the congestion in narrow passages. We evaluate the performance of our approach in complex scenes with tens of agents and narrow passages. We show that our Coupled Global-Local approach and Replanning (CGLR) improves the performance and efficiency over prior decentralized methods. In addition, our approach results in a higher success rate in terms of collision-free navigation to the goals, showing improvement in the range of 3−70% over prior decentralized solutions in certain scenarios.

International Conference on Intelligent Robots and Systems (IROS), 2022