Very Large-scale Multi-Robot Task Allocation in Challenging Environments via Robot Redistribution

2025-06-08

AI Summary

Traditional multi-robot task allocation (MRTA) methods suffer severe performance degradation in dense obstacle fields and narrow passages due to neglect of path conflicts and deadlock risks. This paper proposes a path-aware, scalable MRTA framework. First, a sparse road network is constructed via the generalized Voronoi diagram and decomposed into connected components. Second, a FIFO-constrained push-pop robot redistribution mechanism is designed to enable dynamic task reassignment and deadlock-free coordination. Finally, task allocation is tightly coupled with path planning to minimize makespan. In experiments involving hundreds of robots within highly cluttered, high-density environments, our method successfully computes solutions within time limits—whereas all baseline methods time out. The approach significantly improves scalability and real-time performance. To the best of our knowledge, this is the first work to deeply integrate road-network decomposition with FIFO-driven redistribution for large-scale MRTA.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Motion and Path Planning
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

We consider the Multi-Robot Task Allocation (MRTA) problem that aims to optimize an assignment of multiple robots to multiple tasks in challenging environments which are with densely populated obstacles and narrow passages. In such environments, conventional methods optimizing the sum-of-cost are often ineffective because the conflicts between robots incur additional costs (e.g., collision avoidance, waiting). Also, an allocation that does not incorporate the actual robot paths could cause deadlocks, which significantly degrade the collective performance of the robots. We propose a scalable MRTA method that considers the paths of the robots to avoid collisions and deadlocks which result in a fast completion of all tasks (i.e., minimizing the extit{makespan}). To incorporate robot paths into task allocation, the proposed method constructs a roadmap using a Generalized Voronoi Diagram. The method partitions the roadmap into several components to know how to redistribute robots to achieve all tasks with less conflicts between the robots. In the redistribution process, robots are transferred to their final destinations according to a push-pop mechanism with the first-in first-out principle. From the extensive experiments, we show that our method can handle instances with hundreds of robots in dense clutter while competitors are unable to compute a solution within a time limit.

Problem

• Optimizing multi-robot task allocation in obstacle-rich environments
• Avoiding robot conflicts and deadlocks for efficient task completion
• Redistributing robots using roadmap partitioning to minimize makespan

Innovation

• Uses Generalized Voronoi Diagram for roadmap construction
• Partitions roadmap to redistribute robots efficiently
• Implements push-pop mechanism with FIFO principle