Edge Coverage Path Planning for Robot Mowing

TL;DR

This paper introduces the Edge Coverage Path Planning problem for robot mowing, focusing on efficiently covering lawn edges without losing access to non-convex obstacles, and proposes two novel methods that outperform traditional obstacle dilation techniques.

Contribution

It formulates a new edge-focused coverage planning problem and proposes two innovative methods using morphological processing and geometry, improving coverage accuracy.

Findings

Proposed methods outperform traditional obstacle dilation in coverage accuracy.

Edge-focused planning reduces uncut areas along lawn edges.

New approach maintains access to non-convex obstacle regions.

Abstract

Thanks to the rapid evolvement of robotic technologies, robot mowing is emerging to liberate humans from the tedious and time-consuming landscape work. Traditionally, robot mowing is perceived as a "Coverage Path Planning" problem, with a simplification that converts non-convex obstacles into convex obstacles. Besides, the converted obstacles are commonly dilated by the robot's circumcircle for collision avoidance. However when applied to robot mowing, an obstacle in a lawn is usually non-convex, imagine a garden on the lawn, such that the mentioned obstacle processing methods would fill in some concave areas so that they are not accessible to the robot anymore and hence produce inescapable uncut areas along the lawn edge, which dulls the landscape's elegance and provokes rework. To shrink the uncut area around the lawn edge we hereby reframe the problem into a brand new problem, named the "Edge Coverage Path Planning" problem that is dedicated to path planning with the objective to cover the edge. Correspondingly, we propose two planning methods, the "big and small disk" and the "sliding chopstick" planning method to tackle the problem by leveraging image morphological processing and computational geometry skills. By validation, our proposed methods can outperform the traditional "dilation-by-circumcircle" method.