Optimal Partitioning of Non-Convex Environments for Minimum Turn Coverage Planning

TL;DR

This paper introduces a polynomial-time linear programming method to optimally partition indoor environments into axis-parallel regions, minimizing turns for improved coverage path planning.

Contribution

It presents a novel LP-based approach for optimal environment partitioning that guarantees minimal turns, outperforming heuristic methods in coverage planning.

Findings

The method computes optimal coverage line partitions in polynomial time.

Coverage paths generated show fewer turns compared to state-of-the-art approaches.

Experimental results validate improved coverage efficiency and quality.

Abstract

In this paper, we tackle the problem of planning an optimal coverage path for a robot operating indoors. Many existing approaches attempt to discourage turns in the path by covering the environment along the least number of coverage lines, i.e., straight-line paths. This is because turning not only slows down the robot but also negatively affects the quality of coverage, e.g., tools like cameras and cleaning attachments commonly have poor performance around turns. The problem of minimizing coverage lines however is typically solved using heuristics that do not guarantee optimality. In this work, we propose a turn-minimizing coverage planning method that computes the optimal number of axis-parallel (horizontal/vertical) coverage lines for the environment in polynomial time. We do this by formulating a linear program (LP) that optimally partitions the environment into axis-parallel ranks (non-intersecting rectangles of width equal to the tool width). We then generate coverage paths for a set of real-world indoor environments and compare the results with state-of-the-art coverage approaches.