Hierarchical Trajectory (Re)Planning for a Large Scale Swarm

TL;DR

This paper presents a hierarchical trajectory replanning method for large-scale swarm robots in cluttered environments, combining centralized and decentralized strategies to improve success rate and real-time performance.

Contribution

It introduces a hierarchical, parallelized path planning framework that effectively avoids deadlocks and collisions in large swarms, with demonstrated success in simulation and physical experiments.

Findings

Successfully replanned trajectories for up to 142 robots in simulation.

Achieved real-time replanning with a 24-robot Crazyflie experiment.

Significantly increased task success rate compared to decentralized methods.

Abstract

We consider the trajectory replanning problem for a large-scale swarm in a cluttered environment. Our path planner replans for robots by utilizing a hierarchical approach, dividing the workspace, and computing collision-free paths for robots within each cell in parallel. Distributed trajectory optimization generates a deadlock-free trajectory for efficient execution and maintains the control feasibility even when the optimization fails. Our hierarchical approach combines the benefits of both centralized and decentralized methods, achieving a high task success rate while providing real-time replanning capability. Compared to decentralized approaches, our approach effectively avoids deadlocks and collisions, significantly increasing the task success rate. We demonstrate the real-time performance of our algorithm with up to 142 robots in simulation, and a representative 24 physical Crazyflie nano-quadrotor experiment.