Line Coverage with Multiple Robots: Algorithms and Experiments

TL;DR

This paper introduces efficient algorithms for multi-robot line coverage in environments modeled as graphs, addressing NP-hardness with heuristics and demonstrating effectiveness through experiments on road networks and aerial robots.

Contribution

It presents the Merge-Embed-Merge heuristic algorithm for multi-robot line coverage, incorporating turning costs and nonholonomic constraints, with proven efficiency and scalability.

Findings

The MEM algorithm effectively solves large graph coverage problems.

Incorporating turning costs improves route efficiency.

Experimental results validate the algorithm's practical applicability.

Abstract

The line coverage problem involves finding efficient routes for the coverage of linear features by one or more resource-constrained robots. Linear features model environments like road networks, power lines, and oil and gas pipelines. Two modes of travel are defined for robots: servicing and deadheading. A robot services a feature if it performs task-specific actions, such as taking images, as it traverses the feature; otherwise, it is deadheading. Traversing the environment incurs costs (e.g., travel time) and demands on resources (e.g., battery life). Servicing and deadheading can have different cost and demand functions, which can be direction-dependent. The environment is modeled as a graph, and an integer linear program is provided. As the problem is NP-hard, we design a fast and efficient heuristic algorithm, Merge-Embed-Merge (MEM). Exploiting the constructive property of the MEM algorithm, algorithms for line coverage of large graphs with multiple depots are developed. Furthermore, turning costs and nonholonomic constraints are efficiently incorporated into the algorithm. The algorithms are benchmarked on road networks and demonstrated in experiments with aerial robots.