Search papers, labs, and topics across Lattice.
This paper investigates decentralized task sharing among heterogeneous robots using a load balancing mechanism called "bucket brigades" to optimize throughput in a confined one-dimensional space. By employing a local stabilization technique involving a "token" that decelerates robots post-collision, the authors effectively mitigate chaotic behavior and achieve rapid convergence to a stable system state. Event-driven simulations demonstrate the system's robustness against various perturbations, highlighting its potential for practical applications in coordinated motion planning.
Localized load balancing with minimal communication enables heterogeneous robot teams to achieve robust coordination and rapid convergence in chaotic environments.
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.
