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

TL;DR

This paper introduces a decentralized multi-agent navigation method that combines Voronoi cells and congestion metrics for efficient, collision-free path planning in dense environments, especially with narrow passages.

Contribution

It proposes a novel congestion metric-based replanning approach that couples local and global planning, improving navigation success and efficiency in complex scenes.

Findings

Higher success rate in collision-free navigation

Improved efficiency over prior methods

Effective in dense warehouse-like environments

Abstract

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 warehouse-like scenes and demonstrate improved performance and efficiency over prior methods. In addition, our approach results in a higher success rate in terms of collision-free navigation to the goals.