面向复杂大部件装配的多机器人制造系统运动规划研究进展

吴志鹏; 赵安安; 郑炜; 田威; 崔海华

doi:10.13433/j.cnki.1003-8728.20200146

面向复杂大部件装配的多机器人制造系统运动规划研究进展

doi: 10.13433/j.cnki.1003-8728.20200146

吴志鹏^1,,
赵安安¹,
郑炜¹,
田威^2, ,,
崔海华²

1.
西安飞机工业(集团)有限责任公司, 西安 710089
2.
南京航空航天大学机电学院, 南京 210016

详细信息

作者简介:
吴志鹏(1974-), 博士研究生, 研究方向为飞机数字化装配, 能源与环保, wzp47@avic.com

通讯作者:
田威, 教授, 博士生导师, tw_nj@nuaa.edu.cn

中图分类号: V262.4
计量
- 文章访问数: 291
- HTML全文浏览量: 165
- PDF下载量: 31
- 被引次数: 0
出版历程
- 收稿日期: 2019-10-31
- 刊出日期: 2021-06-01

Research of Motion Planning of Multi-robot Manufacturing System for Assembly of Flexible Large Components

1.
AVIC Xi'an Aircraft Industry(Group)Company LTD., Xi'an 710089, China
2.
College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

摘要

摘要: 移动机器人单元精确、稳定的运动规划是其自主完成既定任务的基础和前提，也是当前多机器人制造系统应用研究所需要解决的技术难题。通过对目前已有多机器人制造系统的研究综述，介绍了运动规划算法的研究现状及其趋势，分析了各种算法优劣势，为多机器人制造系统的工业应用提供理论基础。讨论了实现多机器人制造系统工业应用所需解决的问题，并对多机器人系统及其运动规划方法的研究重点及未来的发展方向进行了展望。
- 航空航天大部件 /
- 规划算法 /
- 运动规划 /
- 经典规划算法 /
- 智能规划算法
Abstract: Accurate and stable motion planning of mobile machine unit is the basis and prerequisite for its autonomous completion of the established tasks. It is a technical problem to be solved in the application research of multi-robot manufacturing system. By reviewing the existing research of the multi-robot manufacturing systems, the current research state and trends of motion planning algorithms are mainly introduced in this paper. The advantages and disadvantages of various algorithms are analyzed to provide the theoretical basis for the industrial application of multi-robot manufacturing systems. The problems needed to be solved to realize the industrial application of multi-robot manufacturing system are discussed. The research emphases the future development directions of multi-robot system and its motion planning methods are prospected.
- aviation and aerospace components /
- planning algorithms /
- motion planning /
- classical planning algorithms /
- heuristic-based algorithms

HTML全文

图 1 Chen建立的KUKA iiwa移动机器人运动学模型^[13]

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图 2 移动机器人装配车间组织原理图

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图 3 优先级资源分配模拟流程图

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图 4 移动机器人单元

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图 5 经典算法和智能算法在实际应用中的占比变化

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图 6 目标和障碍物的吸引力和排斥力

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表 1 冗余自由度机器人速度层运动控制方法总结

方法	关节位置约束	关节速度约束	关节加速度约束	是否完成轨迹跟踪任务
Flacco^[16]	是	是	是	是
Zhang^[17]	是	是	是	是
Faroni^[18]	是	是	是	是
Jia^[19]	是	是	否	是
Li^[20]	否	是	否	是

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表 2 规划算法分类

经典规划算法	人工智能(自然启发)算法
1.势场法(Potential field method)(1979)	1.神经网络算法(Neural network technique)(1943)
2.单元分解法(Cell decomposition)	2.模糊逻辑算法(Fuzzy logic technique)(1965)
3.栅格法(Gird method, GM)(1968)	3.遗传算法(Genetic algorithm technique)(1989)
4.随机路图法(Probabilistic roadmap, PRM)(1996)	4.蚁群算法(Ant colony optimization technique)(1992)
5.快速搜索随机树法(Rapidly exploring random tree, RRT)(1998)	5.粒子群算法(Particle swarm optimization)(2002)
6.虚拟阻抗法(Virtual impedance method)	7.人工蜂群算法(Bee colony optimization technique)(2008)
7.递归与分治法(Divide and conquer method)	9.灰狼优化算法(Grey wolf optimization)(2014)

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表 3 势场法在移动机器人路径规划中的应用

算法名称	作者	年份	障碍物形状	目标点状态	是否经过实物验证
PFM	Tan	2010	多种障碍物	静态目标点	是
New PFM	Ge	2002	点状	动态目标点	否
	Huang	2009	环状	静态目标点	否
	Huang	2012	环状	静态目标点	否
	Valbuena	2012	立方体	静态目标点	是
Path-Guided APF-SR	Chiang	2015	方形	动静态皆有	否
GA-Fuzzy, GA-NN, PFM	Hui	2009	环形	动态目标点	是

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