End-to-End Framework for Robot Lawnmower Coverage Path Planning using Cellular Decomposition

TL;DR

This paper presents an end-to-end framework for robotic lawnmower coverage path planning that integrates user input, cellular decomposition, and adaptive merging to optimize mowing efficiency and coverage.

Contribution

It introduces the AdaptiveDecompositionCPP algorithm, a novel cellular decomposition method with adaptive merging, improving coverage efficiency over existing approaches.

Findings

Effective coverage completeness demonstrated in simulations and real-world tests.

Enhanced mowing efficiency through adaptive cellular merging.

Framework seamlessly converts user inputs into optimized GPS waypoints.

Abstract

Efficient Coverage Path Planning (CPP) is necessary for autonomous robotic lawnmowers to effectively navigate and maintain lawns with diverse and irregular shapes. This paper introduces a comprehensive end-to-end pipeline for CPP, designed to convert user-defined boundaries on an aerial map into optimized coverage paths seamlessly. The pipeline includes user input extraction, coordinate transformation, area decomposition and path generation using our novel AdaptiveDecompositionCPP algorithm, preview and customization through an interactive coverage path visualizer, and conversion to actionable GPS waypoints. The AdaptiveDecompositionCPP algorithm combines cellular decomposition with an adaptive merging strategy to reduce non-mowing travel thereby enhancing operational efficiency. Experimental evaluations, encompassing both simulations and real-world lawnmower tests, demonstrate the effectiveness of the framework in coverage completeness and mowing efficiency.