AUTHOR=Cui Xiaohui , Wang Yu , Yang Shijie , Liu Hanzhang , Mou Chao 

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

JOURNAL=Frontiers in Neurorobotics

VOLUME=Volume 16 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/neurorobotics/articles/10.3389/fnbot.2022.1105177

DOI=10.3389/fnbot.2022.1105177

ISSN=1662-5218

ABSTRACT=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 effectiveness of the collected data. Therefore, this work first analyzes the problem data collected from ground-fixed devices in a complicated forest environment and then considers all significant UAV performance and environmental factors for developing multi-point and two-point UAV path planning strategies with the objective function of maximizing the amount of fresh information. A UAV path planning method based on simulated deterioration is proposed for the multi-point path planning scenario. Then, we adopt chaotic initialization and co-evolutionary techniques to solve the two-point path planning issue. In the experiment, the paper uses benchmark functions to choose an appropriate parameter configuration for the suggested approach. On simulated simple and complicated maps, we evaluate the effectiveness of the proposed method compared to the existing path-planning 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.