双臂系统协同控制研究综述: 从经典到基于学习的算法  

From classic to learning-based algorithms:A survey of cooperative control for dual-arm systems

作　　者：王东 邱超超 贺威[2] 王伟[1] WANG Dong;QIU Chao-chao;HE Wei;WANG Wei(School of Control Science and Engineering,Dalian University of Technology,Dalian Liaoning 116024,China;School of Intelligence Science and Technology,University of Science and Technology Beijing,Beijing 100083,China)

机构地区：[1]大连理工大学控制科学与工程学院,辽宁大连116024 [2]北京科技大学智能科学与技术学院,北京100083

出　　处：《控制理论与应用》2024年第11期1951-1964,共14页Control Theory & Applications

基　　金：国家重点研发计划项目(2019YFE0197700);国家自然科学基金项目(61973050,62173061);辽宁省兴辽英才计划项目(XLYC2007010)资助.

摘　　要：双臂系统的协同控制技术在机器人领域发挥重要的作用,它可以协助人类在工业生产、家庭服务、太空及深海等非结构化环境完成复杂危险的任务.然而,双臂机器人是一个强耦合、高度非线性和不确定性系统,其协同控制问题是一个具有挑战性的课题.本综述首先回顾了双臂系统的发展历程.其次对该系统的结构、建模、控制及其应用进行介绍.特别地,详细归纳了双臂系统的协同控制方法:如协同搬运时的主从控制、力/位混合控制和阻抗控制等经典方法、基于神经网络和模糊系统的智能控制方法和基于强化学习的数据驱动方法在机器人控制取得的最新进展.最后,展望了双臂系统的未来发展趋势.The cooperative control of dual-arm systems(DAS)is a technology that plays an important role in the field of robotics.It can assist human beings to perform complex and dangerous tasks in unstructured environments such as industrial production,domestic life,space and underwater.However,the aforementioned technology is a challenging topic due to difficulties in the design of controllers,which is caused by strong coupling,high nonlinearity and uncertainties.In this survey article,we first review the development of dual-arm systems,and then give an introduction of the structure,modelling,control and application of the systems.In particular,the cooperative control method of the dual-arm systems is concluded in detail:first,classic methods such as master-slave control,force/position hybrid control and impedance control during collaborative manipulation;and second,intelligent control method based on neural network and fuzzy system;third,the latest progress of data-driven methods based on reinforcement learning in robot control.Finally,the future development trends of dual-arm systems are envisioned.

关 键 词：双臂系统 仿人机器人 协同控制 智能控制 强化学习 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置] TP273[自动化与计算机技术—控制科学与工程]