CAP算法实时构建环境图，提升未知区域全覆盖效率。
CAP: A Connectivity-Aware Hierarchical Coverage Path Planning Algorithm for Unknown Environments using Coverage Guidance Graph
- 通过增量构建覆盖引导图，动态感知环境
- 覆盖率提升，路径长度与重叠率显著降低
- 适合需高效全覆盖的机器人任务场景
高效覆盖未知环境需要机器人根据机载传感器数据实时调整路径。本文提出一种连通性感知的分层覆盖路径规划算法CAP，用于未知环境的高效覆盖。在线运行时，CAP增量构建覆盖引导图以捕捉环境关键信息；基于更新后的图，分层规划器生成路径，最大化全局覆盖效率并最小化局部覆盖时间。通过高保真仿真及机器人实验，与五种基线算法对比验证了CAP性能。结果表明，CAP在覆盖时间、路径长度和路径重叠率方面均有显著改善。
原文摘要 · Abstract (English)
Efficient coverage of unknown environments requires robots to adapt their paths in real time based on on-board sensor data. In this paper, we introduce CAP, a connectivity-aware hierarchical coverage path planning algorithm for efficient coverage of unknown environments. During online operation, CAP incrementally constructs a coverage guidance graph to capture essential information about the environment. Based on the updated graph, the hierarchical planner determines an efficient path to maximize global coverage efficiency and minimize local coverage time. The performance of CAP is evaluated and compared with five baseline algorithms through high-fidelity simulations as well as robot experiments. Our results show that CAP yields significant improvements in coverage time, path length, and path overlap ratio.
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