Distributed Sweep Coverage Algorithm of Multi-agent Systems Using Workload Memory

TL;DR

This paper presents a distributed algorithm for multi-agent systems to efficiently perform sweep coverage in uncertain regions by partitioning workloads and balancing subregion tasks, ensuring stability and near-optimal performance.

Contribution

A novel distributed sweep coverage algorithm that partitions workloads, balances subregions, and guarantees system stability in uncertain environments.

Findings

The proposed algorithm is input-to-state stable.

Theoretical bounds on sweep time error are established.

Numerical simulations confirm effectiveness of the approach.

Abstract

This paper addresses the sweep coverage problem of multi-agent systems in uncertain regions. A new formulation of distributed sweep coverage is proposed to cooperatively complete the workload in the uncertain region. Specifically, each agent takes part in partitioning the whole region while sweeping its own subregion. In addition, the partition operation is carried out to balance the workload in subregions. The trajectories of partition points of agents form the boundaries between adjacent sub-regions. Moreover, it is proved that multi-agent system with the proposed control algorithm is input-to-state stable. Theoretical analysis is conducted to obtain the upper bound of the error between the actual sweep time and the optimal sweep time. Finally, numerical simulations demonstrate the effectiveness of the proposed approach.