Fast-Revisit Coverage Path Planning for Autonomous Mobile Patrol Robots Using Long-Range Sensor Information

TL;DR

This paper introduces FaRe-CPP, a novel greedy heuristic and random search-based algorithm for coverage path planning of UGVs with long-range sensors, significantly reducing revisit times and path lengths in simulated industrial patrol scenarios.

Contribution

The paper presents a new Fast-Revisit Coverage Path Planning algorithm combining greedy and random search techniques for efficient UGV patrols with long-range sensors.

Findings

At least 21% reduction in path length.

At least 33% reduction in revisit time.

Validated in Gazebo simulation with TurtleBot3.

Abstract

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.