重大装备制造多机器人任务分配与运动规划技术研究综述

张振国; 毛建旭; 谭浩然; 王耀南; 张雪波; 江一鸣

doi:10.16383/j.aas.c220957

重大装备制造多机器人任务分配与运动规划技术研究综述

doi: 10.16383/j.aas.c220957

张振国^{1, 2, 3,},
毛建旭^{1, 2, 3,},
谭浩然^{1, 2, 3,},
王耀南^{1, 2, 3,},
张雪波^4,,
江一鸣^{2, 3, 5,}

1.
湖南大学电气与信息工程学院长沙 410082
2.
机器人视觉感知与控制技术国家工程研究中心长沙 410082
3.
湘江实验室长沙 410205
4.
南开大学人工智能学院天津 300350
5.
湖南大学机器人学院长沙 410082

基金项目: 国家自然科学基金 (62133005, 62293510, 62293513, 62103138, 62203161), 湖南省科技重大专项 (2021GK1010), 湖南省杰出青年科学基金(2023JJ10015), 湘江实验室重大项目(22xj01006), 湘江实验室一般项目(22xj03002)资助

详细信息

作者简介:
张振国：湖南大学电气与信息工程学院博士研究生. 2022年获得福州大学硕士学位. 主要研究方向为多机器人运动规划与协同控制. E-mail: zhangzhenguo@hnu.edu.cn

毛建旭：湖南大学电气与信息工程学院教授. 主要研究方向为智能机器人系统, 机器人视觉与图像处理. E-mail: maojianxu@hnu.edu.cn

谭浩然：湖南大学电气与信息工程学院副教授, 机器人视觉感知与控制技术国家工程研究中心研究员. 主要研究方向为机器人智能控制与网络化控制系统. 本文通信作者. E-mail: tanhaoran@hnu.edu.cn

王耀南：中国工程院院士, 湖南大学电气与信息工程学院教授. 1995年获湖南大学博士学位. 主要研究方向为机器人学, 智能控制和图像处理. E-mail: yaonan@hnu.edu.cn

张雪波：南开大学人工智能学院机器人与信息自动化研究所教授. 主要研究方向为智能机器人, 包括建图定位、运动规划、视觉伺服、人智协同. E-mail: zhangxuebo@nankai.edu.cn

江一鸣：湖南大学机器人学院副教授, 机器人视觉感知与控制技术国家工程研究中心副研究员. 主要研究方向为多机器人协同控制及应用. E-mail: ymjiang@hnu.edu.cn

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出版历程
- 收稿日期: 2022-12-12
- 录用日期: 2023-04-13
- 网络出版日期: 2023-05-09
- 刊出日期: 2024-01-29

A Review of Task Allocation and Motion Planning for Multi-robot in Major Equipment Manufacturing

ZHANG Zhen-Guo^{1, 2, 3
,},
MAO Jian-Xu^{1, 2, 3
,},
TAN Hao-Ran^{1, 2, 3
,},
WANG Yao-Nan^{1, 2, 3
,},
ZHANG Xue-Bo^4
,,
JIANG Yi-Ming^{2, 3, 5
,}

1.
College of Electrical and Information Engineering, Hunan University, Changsha 410082
2.
National Engineering Research Center of Robot Visual Perception and Control Technology, Changsha 410082
3.
Xiangjiang Laboratory, Changsha 410205
4.
College of Artificial Intelligence, Nankai University, Tianjin 300350
5.
School of Robotics, Hunan University, Changsha 410082

Funds: Supported by National Natural Science Foundation of China (62133005, 62293510, 62293513, 62103138, 62203161), Construction of Innovative Provinces in Hunan Province (2021GK1010), Natural Science Foundation of Hunan Province (2023JJ10015), Major Project of Xiangjiang Laboratory (22xj01006), and Open Project of Xiangjiang Laboratory (22xj03002)

More Information

Author Bio:
ZHANG Zhen-Guo　Ph.D. candidate at the College of Electrical and Information Engineering, Hunan University. He received his master degree from Fuzhou University in 2022. His research interest covers multi-robot motion planning and cooperative control

MAO Jian-Xu　Professor at the College of Electrical and Information Engineering, Hunan University. His research interest covers intelligent robot system, machine vision and image processing

TAN Hao-Ran　Associate professor at the College of Electrical and Information Engineering, Hunan University, research fellow at the National Engineering Research Center of Robot Visual Perception and Control Technology. His research interest covers robot intelligent control and networked control systems. Corresponding author of this paper

WANG Yao-Nan　Academician at Chinese Academy of Engineering, professor at the College of Electrical and Information Engineering, Hunan University. He received his Ph.D. degree from Hunan University in 1995. His research interest covers robotics, intelligent control, and image processing

ZHANG Xue-Bo　Professor at the Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai University. His research interest covers intelligent robotics, including mapping and localization, motion planning, visual servoing and human-AI collaboration

JIANG Yi-Ming　Associate professor at the School of Robotics, Hunan University, associate research fellow at the National Engineering Research Center of Robot Visual Perception and Control Technology. His research interest covers multiple robots cooperative control and their application

摘要

摘要: 飞机蒙皮、船舶舱体、高铁车身等大型复杂部件高效高品质制造是航空航天、海洋舰船、轨道交通等领域重大装备发展的根基. 大型复杂部件具有尺寸超大、型面复杂等特点, 传统的人工、单机制造面临着效率低、一致性差、空间有限等问题, 多机器人具有高鲁棒性、高效性等优点, 为大型复杂部件制造提供了良好的制造基础. 任务分配与运动规划是多机器人制造系统的决策中枢, 其性能影响整个系统的运行效率. 考虑到重大装备部件制造任务分配与运动规划过程中任务工序多、冲突干涉多、精度需求高等挑战, 本文首先对复杂环境下多机器人任务分配与运动规划的重要性进行了说明; 然后阐述了目前主要的任务分配与运动规划方法, 包括其在智能制造领域下的应用; 在此基础上, 对现阶段复杂场景下任务分配和运动规划存在的问题进行了分析, 并使用强化学习与混合优化算法等方法提出了解决思路; 最后对重大装备大型复杂部件制造过程多机器人任务分配和动态规划技术及应用的发展进行了总结与展望.
- 重大装备制造 /
- 多机器人 /
- 任务分配 /
- 运动规划
Abstract: Large-scale complex components such as aircraft skin, ship cabin body, and high-speed rail body are manufactured with high-efficiency and high-quality, which is the foundation for developing major equipment such as aerospace, marine vessels, and rail transportation. Large-scale complex components have the characteristics of large size, complex shapes, traditional manual and single-robot manufacturing are faced with the problems of low efficiency, poor consistency, limited space, etc., multi-robot has the advantages of high robustness and efficiency and is widely used in large-scale complex components. Task allocation and motion planning constitute the decision center of multi-robot systems, whose performance affects the whole system’s efficiency. Firstly, considering the challenges in task allocation and motion planning of major equipment components manufacturing, such as multiple task processes, multiple conflict interference, and high precision requirements, the importance of task allocation and motion planning of multiple robots in complex environments is explained. Then the existing main methods task allocation and motion planning methods are described, including their applications in intelligent manufacturing. On this basis, the existing problems affecting task allocation and motion planning in complex scenarios are analyzed. The solution is proposed using reinforcement learning and a hybrid optimization algorithm. Finally, the development of task allocation and dynamic planning technology and the application of multi-robot in the large-scale complex components manufacturing processes prospect.
- Major equipment manufacturing /
- multi-robot /
- task allocation /
- motion planning

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图 1 重大装备加工需求

Fig. 1 Major equipment processing needs

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图 2 任务分配与运动规划在多机器人制造过程中的重要性

Fig. 2 The importance of task allocation and motion planning in multi-robot manufacturing

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图 3 多机器人任务分配问题

Fig. 3 Task allocation problem of multi-robot

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图 4 集中式任务分配系统

Fig. 4 Centralized task allocation system

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图 5 分布式任务分配系统

Fig. 5 Distributed task allocation system

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图 6 基于线性规划的任务分配方法^{[16-17, 33]}

Fig. 6 Task allocation method based on linear programming^{[16-17, 33]}

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图 7 基于启发式算法的任务分配^{[34, 40]}

Fig. 7 Task assignment based on heuristic algorithm^{[34, 40]}

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图 8 基于学习的任务分配方法^[46-49]

Fig. 8 Task allocation method based on learning^[46-49]

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图 9 基于混合算法的任务分配方法^[50-53]

Fig. 9 Task allocation method based on hybrid algorithm^[50-53]

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图 10 不同数量机器人情况下区域划分^[54]

Fig. 10 Area division under different number of robots^[54]

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图 11 复杂作业场景下多机器人任务分配^{[56, 58]}

Fig. 11 Multi-robot task allocation in complex operation scenarios^{[56, 58]}

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图 12 多机器人跨区域任务分配^[61-62]

Fig. 12 Multi-robot cross-region task allocation^[61-62]

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图 13 复杂场景下多机器人运动规划

Fig. 13 Motion planning of multi-robot in complex scenarios

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图 14 基于搜索的运动规划^[78-82]

Fig. 14 Search-based motion planning^[78-82]

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图 15 基于人工势场法的运动规划^{[88, 91]}

Fig. 15 Motion planning based on artificial potential field^{[88, 91]}

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图 16 基于采样的运动规划^{[96, 99, 102]}

Fig. 16 Motion planning based on sampling^{[96, 99, 102]}

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图 17 基于人工智能的运动规划^{[108, 114, 120]}

Fig. 17 Artificial intelligence-based motion planning^{[108, 114, 120]}

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图 19 多机器人移动端运动规划^[135]

Fig. 19 Motion planning of multi-robot mobile terminals^[135]

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图 18 基于混合算法的运动规划^[122-129]

Fig. 18 Motion planning based on hybrid algorithm^[122-129]

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图 20 非完整和任务约束下作业端路径规划^[136-138]

Fig. 20 Path planning for operators under non-integrity and task constraints^[136-138]

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图 21 复杂环境下集群机器人任务分配与运动规划技术路线

Fig. 21 Technical route of task allocation and motion planning for cluster robots in complex environment

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表 1 任务分配方法分析

Table 1 The analysis of task allocation

使用方法	执行模式	优点	缺点	应用
混合线性规划^[28−33]	集中式	高严谨性、可获得全局最优解	随着问题规模复杂度呈指数增长	时间最优问题
模拟退火法^[34−36]	分布式	计算简单、鲁棒性高	对初始值和参数过于敏感	异构任务分配
群体智能算法^[37−40]	分布式	加快收敛速度、快速找到近似最优解	非常大的解空间时无法保证最优性	提高空间覆盖率
基于市场的方法^[41−44]	分布式	高鲁棒性、可扩展性和灵活性	成本函数缺乏形式化、设计复杂化	飞机装配任务
基于学习的方法^[46−48]	集中式	高适应性、鲁棒性	奖励函数设计困难、采样效率低	未知环境分配
线性规划+市场法^[16]	集中/分布	高严谨性、提高速度	成本函数设计复杂	汽车制造
市场法+启发式^[51]	分布式	高鲁棒性、快速性	无法保证全局最优	焊接制造
启发式+机器学习^[53]	集中/分布	快速性、高适应性	奖励函数设计复杂	工具切换

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表 2 运动规划方法分析

Table 2 The analysis of motion planning

使用方法	全局/局部	优点	缺点	应用
广度优先搜索^[71]	全局	可以获得问题的最优解	浪费大量的时间探索无用区域	游戏领域
A* 算法^{[73, 78−79]}	全局	减少节点, 可获得最优解	仅针对环境已知的静态场景	协同避障
D* 算法^{[74, 80]}	全局	可适应环境信息的变化	寻路效率低、仅适用于低维空间	动态环境
CBS算法^[81−83]	全局	提高搜索速度, 保持最优	依赖地图网格的划分	冲突预测
人工势场法^[84−91]	局部	高鲁棒性、目标路径安全平稳	容易陷入局部最优仅适用于二维平面	协同避障
RRT算法^[95−96]	局部	位置环境探索能力强	规划路径可能非最优解	编队规划
RRT* 算法^[98−101]	局部	可以达到最优路径	增加碰撞检测计算节点	动态环境避障
FMT* 算法^[102]	局部	适用于高位复杂环境	容易产生冗余扩展导致性能较低	高维复杂环境
群体智能算法^[107−114]	全局	可扩展性、灵活性	高随机性、可能不收敛	未知环境搜索
人工神经网络^[115−120]	全局	高适应性、可达到全局最优	奖励函数设计困难、计算效率低	复杂未知环境
冲突搜索 + 市场法^[122]	全局/局部	减少计算时间、设计失误	成本函数设计复杂	多机点焊
分布式梯度 + 群体智能^[128]	全局/局部	高灵活性、最优性	寻路效率低	自主装配

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