移动机器人避障运动研究

doi:10.16182/j.issn1004731x.joss.23-1297E

系统仿真学报 ›› 2024, Vol. 36 ›› Issue (1): 1-26.doi: 10.16182/j.issn1004731x.joss.23-1297E

• 专家约稿 • 下一篇

移动机器人避障运动研究

唐昀超¹^,²(), 祁少军², 朱立学², 卓献荣², 张芸齐², 孟繁²

^1.东莞理工学院，广东东莞 523419
^2.仲恺农业工程学院，广东广州 510650

收稿日期:2023-10-27 修回日期:2023-12-18 出版日期:2024-01-20 发布日期:2024-01-19

Obstacle Avoidance Motion in Mobile Robotics

Tang Yunchao¹^,²(), Qi Shaojun², Zhu Lixue², Zhuo Xianrong², Zhang Yunqi², Meng Fan²

^1.Dongguan University of Technology, Dongguan 523419, China
^2.Zhongkai University of Agriculture and Engineering, Guangzhou 510650, China

Received:2023-10-27 Revised:2023-12-18 Online:2024-01-20 Published:2024-01-19
About author:Tang Yunchao (1983-)， male， professor， doctor， research areas： Computer vision， Field robotics. E-mail： ryan.twain@gmail.com
Tang YunchaoNamed as one of the "Global Top 2% Scientists" by Elsevier in 2021 and 2022, reported as a featured scientist on CCTV-10's "Innovation Time" program, Dr. Tang specializes in research on robotics, structural information perception, and intelligent construction. He has led five national, provincial, and ministerial-level projects, including the National Natural Science Foundation and Postdoctoral Foundation, as well as over 20 projects at the departmental and corporate level. As a first/corresponding author, he has published over 40 SCI papers, including 9 ESI hot papers, with an h-index of 33. Dr. Tang has been awarded six prizes, including the first prize in the Guangdong Province Science and Technology Award for Measurement, Control, and Instrumentation. He serves as an editorial board member for five SCI journals, including Frontiers in Materials, Journal of Sensors, and Buildings, and guest editor for several SCI top-tier journals.
Supported by:
National Natural Science Foundation(52368028)

摘要/Abstract

摘要：

随着人工智能技术的飞速进展，移动机器人在工业、航天及农业等领域的作用逐渐凸显，其自主避障能力直接关系到在不同环境中作业的安全性与效率，而路径规划作为避障的核心技术之一，在决定避障性能方面起着至关重要的作用。对移动机器人路径规划技术进行了综述。基于作业需求将算法分为全局规划和局部避障两类。详述了以采样、图搜索和仿生学为基础的全局规划方法，分析了其收敛速度、内存需求及计算效率，并探讨了其改进策略。对局部避障算法进行了探讨，概述了其原理与特点，并明确其最佳应用场景。对当前的自主避障技术进行了总结，强调了传统算法的智能化程度仍需提升，以及集成不同的算法以提高规划性能将是未来的发展大势。

关键词: 移动机器人, 避障运动, 全局路径规划, 局部避障算法

Abstract:

The advancement of artificial intelligence technology has significantly enhanced the utilization of mobile robots in various fields such as industry, aerospace, and agriculture. The autonomous obstacle avoidance capability of these robots is crucial to the safety and efficiency of their operations in diverse settings. Path planning, a key technology in obstacle avoidance, plays an essential role in the overall performance of these systems. This paper presents a comprehensive review of path planning technology for mobile robots, categorizing the algorithms into global planning and local obstacle avoidance according to their operational requirements. Specific focus is given to the global planning methods involving sampling, graph search, and biomimetics, assessing their convergence rate, memory demands, and computational efficiency, along with strategies for improvement. The paper then explores local obstacle avoidance algorithms, explicating their foundational principles, characteristics, and ideal use cases. In conclusion, the paper synthesizes the state-of-the-art in autonomous obstacle avoidance, noting that the strategic integration of various algorithms to refine planning performance, and the enhancement of traditional algorithms' intelligence is projected to be a leading trend in future research.

Key words: mobile robot, obstacle avoidance motion, global path planning, local obstacle avoidance

中图分类号:

TP242.6

唐昀超,祁少军,朱立学等 . 移动机器人避障运动研究[J]. 系统仿真学报, 2024, 36(1): 1-26.

Tang Yunchao,Qi Shaojun,Zhu Lixue,et al . Obstacle Avoidance Motion in Mobile Robotics[J]. Journal of System Simulation, 2024, 36(1): 1-26.

图/表 12

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移动机器人避障运动研究

Obstacle Avoidance Motion in Mobile Robotics

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