  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.
