Hierarchical Trajectory (Re)Planning for a Large Scale Swarm

2025-01-28

AI Summary

Addressing the challenge of real-time trajectory replanning for large-scale robot swarms in complex environments—requiring collision-free, deadlock-free, dynamically feasible, and computationally efficient solutions—this paper proposes a hierarchical cooperative framework. First, the workspace is partitioned spatially; then, conflict-free paths are computed in parallel within each partition; finally, distributed trajectory optimization, incorporating control feasibility constraints, ensures deadlock immunity and motion smoothness. The framework synergistically integrates centralized coordination with decentralized execution. To our knowledge, it is the first to achieve high-success-rate, real-time, deadlock-free replanning for swarms of over one hundred agents. Extensive simulations demonstrate real-time performance with 142 agents, while physical experiments on Crazyflie nano-quadcopters successfully deploy 24 robots. Compared to purely decentralized approaches, the method achieves significantly higher task success rates, with zero collisions and zero deadlocks throughout all evaluations.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Intelligent Robots: Motion and Path Planning
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Economics, Online Markets and Human Computation: The sharing economy

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.

Problem

• Multi-robot Path Planning
• Collision Avoidance
• Real-time Replanning

Innovation

• Modular Path Planning
• Multi-Robot Systems
• Collision Avoidance