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智能水下机器人路径规划方法综述
A general overview of path planning methods for autonomous underwater vehicle
孙玉山1, 王力锋2, 吴菁2, 冉祥瑞1
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作者单位:1. 哈尔滨工程大学 水下机器人技术重点实验室, 黑龙江 哈尔滨 150001;
2. 中国船舶及海洋工程设计研究院, 上海 200011
中文关键字:水下机器人;路径规划;人工势场;群智能;神经网络;强化学习
英文关键字:AUV; path planning; artificial potential field; swarm intelligence; neural network; reinforcement learning
中文摘要:路径规划方法是智能水下机器人技术研究的核心内容之一,是实现自主航行和作业的关键环节。本文将水下机器人的路径规划方法按智能程度分为传统和智能两大类。传统方法包括基于路线图构建的路径规划方法、基于单元分解的路径规划方法以及基于人工势场的路径规划方法,智能方法包括基于群智能的路径规划方法和基于机器学习的路径规划方法。针对水下机器人规划环境的特点,分别对这几类典型方法进行总结与评价,重点分析了智能方法的优缺点和关键问题,最后展望智能水下机器人路径规划的未来研究方向。
英文摘要:The path planning is one of the core contents of autonomous underwater vehicle research and it is the key link for autonomous navigation and operation. In this paper, the path planning methods for autonomous underwater vehicle are specifically divided into the traditional and intelligent methods. Traditional methods include path planning based on road map, path planning based on unit decomposition and path planning based on artificial potential field. Intelligent methods include path planning based on swarm intelligence and path planning based on machine learning. In view of the characteristics of the underwater environment, these typical methods are summarized and evaluated respectively. The advantages and disadvantages and key problems of the intelligent methods are analyzed. Finally, the future research direction of the autonomous underwater vehicle path planning method is predicted.
2020,42(4): 1-7 收稿日期:2019-06-04
DOI:10.3404/j.issn.1672-7649.2020.04.001
分类号:TP242.6
作者简介:孙玉山(1973-),男,教授,研究方向为水下机器人导航、控制和规划系统
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