Pass the Bucket: Efficient, Robust, Local Load Balancing for Teams of Heterogeneous Robots

• URL: http://arxiv.org/abs/2608.27085v1
• Date: Thu, 27 Aug 2026 13:09:45 GMT
• Title: Pass the Bucket: Efficient, Robust, Local Load Balancing for Teams of Heterogeneous Robots
• Abstract summary: We study the problem of decentralized, self-organized task sharing for a swarm of heterogeneous robots.<n>We present theoretical and practical results for the simple but effective mechanism of emphbucket brigades for load balancing.
• Score: 2.0329663822294926
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We study the problem of decentralized, self-organized task sharing for a swarm of heterogeneous robots that collaborate in transportation or other objectives that require coordinated motion planning. To this end, we present theoretical and practical results for the simple but effective mechanism of \emph{bucket brigades} for load balancing, in which a team of heterogenous robots share a spatial task in a confined, one-dimensional space, while only being able to sense collisions with neighbors or walls. The goal is to optimize throughput of the overall system, without central control or information, aiming at an interval partition proportional to robot velocities. We address possible chaotic system behavior by developing a stabilization mechanism based on simple local aid, a ``token'', that temporarily decelerates robots after an encounter. This purely local change eliminates persistent oscillations, resulting in convergence towards a stable system state. We accelerate system convergence by comparing a single boundary token to ubiquitous two-directional tokens and optimizing the deceleration factor. Event-driven simulations report convergence times and robustness: For a large variety of perturbations (such as robot deletion, position or velocity jittering), the system reliably re-converges. The results suggest a local, practical mechanism for robust load balancing for heterogeneous teams of robots that promises an effective tool as basis for more complex scenarios.

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