Area Coverage with Multiple Capacity-Constrained Robots

TL;DR

This paper introduces a novel approach for planning coverage routes for multiple capacity-constrained robots, transforming the area coverage problem into a line coverage problem to optimize routes while respecting resource limits.

Contribution

The authors propose a new formulation and algorithms for multi-robot area coverage that handle non-monotone polygons and asymmetric travel costs, improving efficiency over existing methods.

Findings

Routes are on average 10% more cost-effective than state-of-the-art methods.

The approach is validated on 25 indoor and 300 outdoor environments.

Experiments demonstrate effectiveness with UAVs in real-world scenarios.

Abstract

The area coverage problem is the task of efficiently servicing a given two-dimensional surface using sensors mounted on robots such as unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). We present a novel formulation for generating coverage routes for multiple capacity-constrained robots, where capacity can be specified in terms of battery life or flight time. Traversing the environment incurs demands on the robot resources, which have capacity limits. The central aspect of our approach is transforming the area coverage problem into a line coverage problem (i.e., coverage of linear features), and then generating routes that minimize the total cost of travel while respecting the capacity constraints. We define two modes of travel: (1) servicing and (2) deadheading, which correspond to whether a robot is performing task-specific actions or not. Our formulation allows separate and asymmetric travel costs and demands for the two modes. Furthermore, the cells computed from cell decomposition, aimed at minimizing the number of turns, are not required to be monotone polygons. We develop new procedures for cell decomposition and generation of service tracks that can handle non-monotone polygons with or without holes. We establish the efficacy of our algorithm on a ground robot dataset with 25 indoor environments and an aerial robot dataset with 300 outdoor environments. The algorithm generates solutions whose costs are 10% lower on average than state-of-the-art methods. We additionally demonstrate our algorithm in experiments with UAVs.