Discrete Partitioning and Coverage Control for Gossiping Robots

TL;DR

This paper introduces distributed gossip-based algorithms enabling mobile robots to autonomously partition and cover complex non-convex environments efficiently, with proven convergence and scalability demonstrated through simulations and hardware tests.

Contribution

It presents a novel gossip-based distributed approach for partitioning and coverage in non-convex environments, with convergence guarantees and scalability for large robot teams.

Findings

Algorithm converges to pairwise-optimal partition in finite time

Scales well for large teams and environments

Validated through extensive simulations and hardware experiments

Abstract

We propose distributed algorithms to automatically deploy a team of mobile robots to partition and provide coverage of a non-convex environment. To handle arbitrary non-convex environments, we represent them as graphs. Our partitioning and coverage algorithm requires only short-range, unreliable pairwise "gossip" communication. The algorithm has two components: (1) a motion protocol to ensure that neighboring robots communicate at least sporadically, and (2) a pairwise partitioning rule to update territory ownership when two robots communicate. By studying an appropriate dynamical system on the space of partitions of the graph vertices, we prove that territory ownership converges to a pairwise-optimal partition in finite time. This new equilibrium set represents improved performance over common Lloyd-type algorithms. Additionally, we detail how our algorithm scales well for large teams in large environments and how the computation can run in anytime with limited resources. Finally, we report on large-scale simulations in complex environments and hardware experiments using the Player/Stage robot control system.