End-to-End Framework for Robot Lawnmower Coverage Path Planning using Cellular Decomposition
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.
Code (0)
등록된 구현이 없습니다.
Tasks
NavigateMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Informative Path Planning with Guaranteed Estimation Uncertainty
Environmental monitoring robots often need to estimate data fields (e.g., salinity, temperature, bathymetry) under tight resource constraints. Classical boustrophedon lawnmower surveys provide geometric coverage guarante…
Online Concurrent Multi-Robot Coverage Path Planning
Recently, centralized receding horizon online multi-robot coverage path planning algorithms have shown remarkable scalability in thoroughly exploring large, complex, unknown workspaces with many robots. In a horizon, the…
A Planning Framework for Persistent, Multi-UAV Coverage with Global Deconfliction
Planning for multi-robot coverage seeks to determine collision-free paths for a fleet of robots, enabling them to collectively observe points of interest in an environment. Persistent coverage is a variant of traditional…
MDCPP: Multi-robot Dynamic Coverage Path Planning for Workload Adaptation
Multi-robot Coverage Path Planning (MCPP) addresses the problem of computing paths for multiple robots to effectively cover a large area of interest. Conventional approaches to MCPP typically assume that robots move at f…
Priority-Aware Multi-Robot Coverage Path Planning
Multi-robot systems are widely used for coverage tasks that require efficient coordination across large environments. In Multi-Robot Coverage Path Planning (MCPP), the objective is typically to minimize the makespan by g…