Near-Optimal Coverage Path Planning with Turn Costs

TL;DR

This paper introduces a theoretically grounded algorithm for coverage path planning that efficiently handles turn costs, complex environments, and partial coverage, with proven low optimality gaps and practical versatility.

Contribution

It presents a systematic, robust method for coverage path planning in complex polygonal environments, incorporating turn costs and partial coverage considerations.

Findings

Algorithm achieves low optimality gaps in complex environments

Handles partial coverage and heterogeneous passage costs

Demonstrates efficiency and versatility in real-world scenarios

Abstract

Coverage path planning is a fundamental challenge in robotics, with diverse applications in aerial surveillance, manufacturing, cleaning, inspection, agriculture, and more. The main objective is to devise a trajectory for an agent that efficiently covers a given area, while minimizing time or energy consumption. Existing practical approaches often lack a solid theoretical foundation, relying on purely heuristic methods, or overly abstracting the problem to a simple Traveling Salesman Problem in Grid Graphs. Moreover, the considered cost functions only rarely consider turn cost, prize-collecting variants for uneven cover demand, or arbitrary geometric regions. In this paper, we describe an array of systematic methods for handling arbitrary meshes derived from intricate, polygonal environments. This adaptation paves the way to compute efficient coverage paths with a robust theoretical foundation for real-world robotic applications. Through comprehensive evaluations, we demonstrate that the algorithm also exhibits low optimality gaps, while efficiently handling complex environments. Furthermore, we showcase its versatility in handling partial coverage and accommodating heterogeneous passage costs, offering the flexibility to trade off coverage quality and time efficiency.