Stigmergy-based, Dual-Layer Coverage of Unknown Indoor Regions

TL;DR

This paper introduces algorithms for swarm robots to efficiently explore and cover unknown indoor areas using airborne agents as both explorers and beacons, achieving fast, energy-efficient coverage with minimal external information.

Contribution

It proposes a novel dual-layer approach where airborne agents serve as both explorers and beacons, enabling linear-time coverage and improved energy efficiency in unknown indoor environments.

Findings

Linear-time coverage achieved with the dual-layer approach

Energy consumption is lower compared to existing algorithms

Effective coverage confirmed through simulations and proofs

Abstract

We present algorithms for uniformly covering an unknown indoor region with a swarm of simple, anonymous and autonomous mobile agents. The exploration of such regions is made difficult by the lack of a common global reference frame, severe degradation of radio-frequency communication, and numerous ground obstacles. We propose addressing these challenges by using airborne agents, such as Micro Air Vehicles, in dual capacity, both as mobile explorers and (once they land) as beacons that help other agents navigate the region. The algorithms we propose are designed for a swarm of simple, identical, ant-like agents with local sensing capabilities. The agents enter the region, which is discretized as a graph, over time from one or more entry points and are tasked with occupying all of its vertices. Unlike many works in this area, we consider the requirement of informing an outside operator with limited information that the coverage mission is complete. Even with this additional requirement we show, both through simulations and mathematical proofs, that the dual role concept results in linear-time termination, while also besting many well-known algorithms in the literature in terms of energy use.