Cooperative Path Planning for Persistent Surveillance in Large-Scale Environment with UAV-UGV System

Abstract

In this paper, we consider the path planning problem of the Unmanned Air-Ground Vehicle (UAV-UGV) system for large-scale environmental persistent surveillance. The goal is to acquire a set of periodically visited surveillance nodes while minimizing the traveling distance. Some expected key problems are the limitations of UAV speed, UGV speed, UAV endurance, and UAV field of view. To address this issue, the path planning of UAV-UGV surveillance system is modeled as a TSP optimization problem based on multiple constraints, aiming to minimize the total path length of the system to perform the task. UAV is responsible for visiting the surveillance node and UGV serves as a mobile charging station. And a two-layer chaotic aptenodytes forsteri optimization algorithm (Two-CAFO) is proposed to solve this problem. Our solution has been tested in several simulated and real-world environments. The results demonstrate that the proposed Two-CAFO has superior performance compared to other state-of-the-art algorithms in solving the path planning problem for large-scale environmental persistent surveillance tasks of UAV and UGV. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.