提出快速重访路径规划算法，提升巡检机器人覆盖效率。
Fast-Revisit Coverage Path Planning for Autonomous Mobile Patrol Robots Using Long-Range Sensor Information
- 基于贪心启发法生成最大覆盖点，再用随机搜索优化路径。
- 相比现有算法，路径长度减少至少21%，重访时间降低33%。
- 适合工业场景中需高效巡检的移动机器人应用。
无人地面车辆（UGVs）在工业巡检中的应用日益广泛，但其感知系统（如计算机视觉）受限于传感器范围或场地结构。为实现高效巡检，需通过覆盖路径规划（CPP）算法导航所有关键点，并尽快启动下一轮。本文提出新型快速重访覆盖路径规划（FaRe-CPP）算法：采用贪心启发法生成最大覆盖区域的候选点，结合基于随机搜索的路径优化技术，在保证覆盖的前提下最小化重访时间。我们在Gazebo仿真环境和搭载摄像头的TurtleBot3上评估了该算法，与多种现有方法对比，结果显示，平均路径长度减少至少21%，重访时间降低至少33%，显著提升了巡检效率。
原文摘要 · Abstract (English)
The utilization of Unmanned Ground Vehicles (UGVs) for patrolling industrial sites has expanded significantly. These UGVs typically are equipped with perception systems, e.g., computer vision, with limited range due to sensor limitations or site topology. High-level control of the UGVs requires Coverage Path Planning (CPP) algorithms that navigate all relevant waypoints and promptly start the next cycle. In this paper, we propose the novel Fast-Revisit Coverage Path Planning (FaRe-CPP) algorithm using a greedy heuristic approach to propose waypoints for maximum coverage area and a random search-based path optimization technique to obtain a path along the proposed waypoints with minimum revisit time. We evaluated the algorithm in a simulated environment using Gazebo and a camera-equipped TurtleBot3 against a number of existing algorithms. Compared to their average path lengths and revisit times, our FaRe-CPP algorithm showed a reduction of at least 21% and 33%, respectively, in these highly relevant performance indicators.
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