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基于快速动力学辨识的机器人力/位混合控制碰撞检测研究 被引量:12

Collision detection for robot force/position hybrid control based on fast dynamics identification
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摘要 针对机器人力/位混合控制时发生异常碰撞所导致的安全问题,提出一种基于快速动力学辨识的碰撞检测方案。首先,建立机器人快速动力学的辨识模型,基于拉格朗日法整理出机器人关节重力矩的最简三角函数回归矩阵,运用连续摩擦模型建模关节摩擦力;然后,采用最小二乘法分别辨识出机器人关节的重力矩最简参数集和连续摩擦模型参数集;最后,为验证提出方法的有效性,设计并完成机器人快速动力学辨识和碰撞检测实验。结果表明:提出的方法能够快速有效地辨识机器人的动力学参数,可实时检测机器人位置控制方向上4 N·m,力控制方向8 N·m以上的异常碰撞,有效保障操作人员和设备的安全,具有一定的工程参考价值。 Aiming at safety problems caused by abnormal collisions during robot force/position hybrid control,a collision detection scheme based on rapid dynamics identification is proposed.Firstly,a rapid robot dynamics identification model is formulated.The simplest trigonometric function regression matrix of the robot joint gravity moment is sorted based on Lagrangian method,and the continuous friction model is used to model the joint friction.Then,both the minimum parameter set of the gravity moment and the parameter set of the continuous friction model of the robot joint are identified by least square method.Finally,rapid dynamic identification and collision detection experiments of the robot are designed and completed to evaluate the effectiveness of the proposed method.Results show that the proposed method can quickly and effectively identify the dynamic parameters of the robot.The abnormal collisions above 4 N·m in position control and 8 N·m in force control can be detected in real time,which effectively guarantees the safety of operators and equipment and has certain engineering reference value.
作者 张春涛 王勇 穆春阳 李家旺 刘正士 Zhang Chuntao;Wang Yong;Mu Chunyang;Li Jiawang;Liu Zhengshi(College of Mechanical Engineering,Hefei University of Technology,Hefei 23000,China;College of Mechanical and Electrical Engineering,North Minzu University,Yinchuan 750021,China;Beijing Lingsichuangqi Technology,Ltd.,Beijing 10000,China)
出处 《仪器仪表学报》 CSCD 北大核心 2021年第6期161-171,共11页 Chinese Journal of Scientific Instrument
基金 国家自然科学基金(U1713210) 宁夏自然科学基金(2019AAC03121,2020AAC03201)项目资助。
关键词 机器人力/位混合控制 连续摩擦模型 重力辨识 碰撞检测 robot force/position hybrid control continuously friction model gravity recognition collision detection
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