Optimal Allocation of Many Robot Guards for Sweep-Line Coverage

TL;DR

This paper presents a fast, scalable max-flow based algorithm for optimally allocating mobile robots along a sweep line in complex environments, enhancing coverage and search tasks.

Contribution

It introduces a novel max-flow algorithm that efficiently computes robot allocation for sweep-line coverage, generalizing workspace decomposition techniques.

Findings

Algorithm runs in under two seconds for large environments

Demonstrates scalability across diverse obstacle configurations

Achieves effective coverage with fewer robots

Abstract

We study the problem of allocating many mobile robots for the execution of a pre-defined sweep schedule in a known two-dimensional environment, with applications toward search and rescue, coverage, surveillance, monitoring, pursuit-evasion, and so on. The mobile robots (or agents) are assumed to have one-dimensional sensing capability with probabilistic guarantees that deteriorate as the sensing distance increases. In solving such tasks, a time-parameterized distribution of robots along the sweep frontier must be computed, with the objective to minimize the number of robots used to achieve some desired coverage quality guarantee or to maximize the probabilistic guarantee for a given number of robots. We propose a max-flow based algorithm for solving the allocation task, which builds on a decomposition technique of the workspace as a generalization of the well-known boustrophedon decomposition. Our proposed algorithm has a very low polynomial running time and completes in under two seconds for polygonal environments with over $10^5$ vertices. Simulation experiments are carried out on three realistic use cases with randomly generated obstacles of varying shapes, sizes, and spatial distributions, which demonstrate the applicability and scalability our proposed method.