Design and Assessment of Sweep Coverage Algorithms for Multiagent Systems With Online Learning Strategies

Abstract

Cooperative sweep coverage of multiagent systems (MASs) has found broad applications in various fields. This article proposes a scheme to address the sweep coverage problem of MASs within uncertain environments. In the proposed formulation, the coverage region is divided into multiple stripes, of which each has the workload completed by MASs in sequence. When the workload on the current stripe is completed, all the agents switch to the next together. The temporal dependence between the switching time computation and the sweep coverage operation is taken into account, and an online learning strategy is designed to handle environmental uncertainties and balance the workload among agents on the same stripe. Thereby, the distributed sweep coverage algorithm is developed to guarantee the complete sweep coverage, which consists of three operations, i.e., communication, workload partition, and sweeping. Theoretical analysis is afterward conducted to estimate the upper bound for the error between the actual and optimal coverage time. Finally, numerical simulations are carried out to substantiate the effectiveness and superiority of the proposed scheme.