首页|Dynamics Modeling and Parameter Identification for a Coupled-Drive Dual-Arm Nursing Robot

Dynamics Modeling and Parameter Identification for a Coupled-Drive Dual-Arm Nursing Robot

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A dual-arm nursing robot can gently lift patients and transfer them between a bed and a wheelchair.With its light-weight design,high load-bearing capacity,and smooth surface,the coupled-drive joint is particularly well suited for these robots.However,the coupled nature of the joint disrupts the direct linear relationship between the input and output torques,posing challenges for dynamic modeling and practical applications.This study investigated the transmission mechanism of this joint and employed the Lagrangian method to construct a dynamic model of its internal dynamics.Building on this foundation,the Newton-Euler method was used to develop a dynamic model for the entire robotic arm.A continuously differentiable friction model was incorporated to reduce the vibra-tions caused by speed transitions to zero.An experimental method was designed to compensate for gravity,inertia,and modeling errors to identify the parameters of the friction model.This method establishes a mapping relationship between the friction force and motor current.In addition,a Fourier series-based excitation trajectory was developed to facilitate the identification of the dynamic model parameters of the robotic arm.Trajectory tracking experiments were conducted during the experimental validation phase,demonstrating the high accuracy of the dynamic model and the parameter identification method for the robotic arm.This study presents a dynamic modeling and param-eter identification method for coupled-drive joint robotic arms,thereby establishing a foundation for motion control in humanoid nursing robots.

Nursing-care robotCoupled-drive jointDynamic modelingParameter identification

Hao Lu、Zhiqiang Yang、Deliang Zhu、Fei Deng、Shijie Guo

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College of Electronic Information and Automation,Tianjin University of Science and Technology,Tianjin 300222,China

Academy for Engineering & Technology,Fudan University,Shanghai 200433,China

Shanghai Engineering Research Center of AI & Robotics,Shanghai 200433,China

School of Mechanical Engineering,Hebei University of Technology,Tianjin 300130,China

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Shanghai Municipal Science and Technology ProgramNational Key Research and Development Program of China

215111017012021YFC0122704

2024

中国机械工程学报
中国机械工程学会

中国机械工程学报

CSTPCD
影响因子:0.765
ISSN:1000-9345
年,卷(期):2024.37(4)