UAV path planning method for data collection of fixed-point equipment in complex forest environment

计算机科学 运动规划 初始化 实时计算 模拟退火 路径(计算) 水准点(测量) 点(几何) 运筹学 模拟 人工智能 机器学习 机器人 地理 工程类 几何学 数学 程序设计语言 大地测量学
作者
Xiaohui Cui,Yu Wang,Shijie Yang,Hanzhang Liu,Chao Mou
出处
期刊:Frontiers in Neurorobotics [Frontiers Media]
卷期号:16 被引量:3
标识
DOI:10.3389/fnbot.2022.1105177
摘要

In a complicated forest environment, it is usual to install many ground-fixed devices, and patrol personnel periodically collects data from the device to detect forest pests and valuable wild animals. Unlike human patrols, UAV (Unmanned Aerial Vehicles) may collect data from ground-based devices. The existing UAV path planning method for fixed-point devices is usually acceptable for simple UAV flight scenes. However, it is unsuitable for forest patrol. Meanwhile, when collecting data, the UAV should consider the timeliness of the collected data. The paper proposes two-point path planning and multi-point path planning methods to maximize the amount of fresh information collected from ground-fixed devices in a complicated forest environment. Firstly, we adopt chaotic initialization and co-evolutionary algorithmto solve the two-point path planning issue considering all significant UAV performance and environmental factors. Then, a UAV path planning method based on simulated annealing is proposed for the multi-point path planning issue. In the experiment, the paper uses benchmark functions to choose an appropriate parameter configuration for the proposed approach. On simulated simple and complicated maps, we evaluate the effectiveness of the proposed method compared to the existing pathplanning strategies. The results reveal that the proposed ways can effectively produce a UAV patrol path with higher information freshness in fewer iterations and at a lower computing cost, suggesting the practical value of the proposed approach.
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