用自适应分解法优化割草机器人路径，提升复杂地形覆盖效率。
End-to-End Framework for Robot Lawnmower Coverage Path Planning using Cellular Decomposition
- 通过自适应合并策略改进细胞分解算法，减少空驶行程。
- 仿真与实测均显示路径覆盖完整且效率显著提升。
- 适合需高效自主割草的农业或园林场景使用。
高效的全覆盖路径规划（CPP）对自主割草机器人在形状多样且不规则的草坪上有效导航和维护至关重要。本文提出一个端到端的完整流程，将用户在航拍地图上定义的边界自动转化为优化的覆盖路径。该流程包括用户输入提取、坐标转换、基于新型AdaptiveDecompositionCPP算法的区域分解与路径生成、交互式路径预览与定制，以及最终转换为可执行的GPS航点。AdaptiveDecompositionCPP算法结合细胞分解与自适应合并策略，有效减少非割草行驶距离，提升作业效率。实验评估涵盖仿真与真实割草机测试，验证了该框架在覆盖完整性和割草效率方面的有效性。
原文摘要 · Abstract (English)
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.
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