Yao Zhixin, Zhao Chunjiang, Zhang Taihong
College of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, China.
Engineering Research Center of Intelligent Agriculture, Ministry of Education, Urumqi 830052, China.
iScience. 2023 Dec 14;27(2):108714. doi: 10.1016/j.isci.2023.108714. eCollection 2024 Feb 16.
In this paper, we review, compare, and analyze previous studies on agricultural machinery automatic navigation and path planning technologies. First, the paper introduces the fundamental components of agricultural machinery autonomous driving, including automatic navigation, path planning, control systems, and communication modules. Generally, the methods for automatic navigation technology can be divided into three categories: Global Navigation Satellite System (GNSS), Machine Vision, and Laser Radar. The structures, advantages, and disadvantages of different methods and the technical difficulties of current research are summarized and compared. At present, the more successful way is to use GNSS combined with machine vision to provide guarantee for agricultural machinery to avoid obstacles and generate the optimal path. Then the path planning methods are described, including four path planning algorithms based on graph search, sampling, optimization, and learning. This paper proposes 22 available algorithms according to different application scenarios and summarizes the challenges and difficulties that have not been completely solved in the current research. Finally, some suggestions on the difficulties arising in these studies are proposed for further research.
在本文中,我们回顾、比较并分析了以往关于农业机械自动导航和路径规划技术的研究。首先,本文介绍了农业机械自动驾驶的基本组成部分,包括自动导航、路径规划、控制系统和通信模块。一般来说,自动导航技术的方法可分为三类:全球导航卫星系统(GNSS)、机器视觉和激光雷达。总结并比较了不同方法的结构、优缺点以及当前研究的技术难点。目前,较为成功的方法是使用GNSS与机器视觉相结合,为农业机械避障和生成最优路径提供保障。然后描述了路径规划方法,包括基于图搜索、采样、优化和学习的四种路径规划算法。本文根据不同应用场景提出了22种可用算法,并总结了当前研究中尚未完全解决的挑战和困难。最后,针对这些研究中出现的困难提出了一些建议以供进一步研究。