在建立六自由度焊装机器人D-H坐标系及确定各手臂参数的基础上,研究其正逆运动学方程,设计其数值模拟算法,并在MATLAB环境下建立仿真模型,获得在定点控制模式下各个关节电机控制量,得到具有固定重力补偿的机器人控制模型;基于六自由度...在建立六自由度焊装机器人D-H坐标系及确定各手臂参数的基础上,研究其正逆运动学方程,设计其数值模拟算法,并在MATLAB环境下建立仿真模型,获得在定点控制模式下各个关节电机控制量,得到具有固定重力补偿的机器人控制模型;基于六自由度机器人动力学控制原理,在考虑电机特性及机器人手臂动力学特性的基础上,建立了具有传感器反馈控制的优化模型;在Tecnomatix环境下创建了四机器人焊接工位,利用其提供的Robotics功能模块,实现了机器人的路径及功能示教;利用DSP(Digital Single Processor,数字信号处理器)解算了机器人关节坐标的牛顿-欧拉逆运动学方程,并实时、快速处理传感器反馈信号,实现了对多个伺服系统的闭环控制。展开更多
Multibody musculoskeletal modeling of human gait has been proved helpful in investigating the pathology of musculoskeletal disorders.However,conventional inverse dynamics methods rely on external force sensors and can...Multibody musculoskeletal modeling of human gait has been proved helpful in investigating the pathology of musculoskeletal disorders.However,conventional inverse dynamics methods rely on external force sensors and cannot capture the nonlinear muscle behaviors.Meanwhile,the forward dynamics approach is computationally demanding and only suited for relatively simple tasks.This study proposed an integrated simulation methodology to fulfill the requirements of estimating foot-ground reaction force,tendon elasticity,and muscle recruitment optimization.A hybrid motion capture system,which combines the marker-based infrared device and markerless tracking through deep convolutional neural networks,was developed to track lower limb movements.The foot-ground reaction forces were determined by a contact model for soft materials,and its parameters were estimated using a two-step optimization method.The muscle recruitment problem was first resolved via a static optimization algorithm,and the obtained muscle activations were used as initial values for further simulation.A torque tracking procedure was then performed by minimizing the errors of joint torques calculated by musculotendon equilibrium equations and inverse dynamics.The proposed approach was validated against the electromyography measurements of a healthy subject during gait.The simulation framework provides a robust way of predicting joint torques,musculotendon forces,and muscle activations,which can be beneficial for understanding the biomechanics of normal and pathological gait.展开更多
文摘在建立六自由度焊装机器人D-H坐标系及确定各手臂参数的基础上,研究其正逆运动学方程,设计其数值模拟算法,并在MATLAB环境下建立仿真模型,获得在定点控制模式下各个关节电机控制量,得到具有固定重力补偿的机器人控制模型;基于六自由度机器人动力学控制原理,在考虑电机特性及机器人手臂动力学特性的基础上,建立了具有传感器反馈控制的优化模型;在Tecnomatix环境下创建了四机器人焊接工位,利用其提供的Robotics功能模块,实现了机器人的路径及功能示教;利用DSP(Digital Single Processor,数字信号处理器)解算了机器人关节坐标的牛顿-欧拉逆运动学方程,并实时、快速处理传感器反馈信号,实现了对多个伺服系统的闭环控制。
基金the National Natural Science Foundations of China(Grant Nos.12102035 and 12125201)the China Postdoctoral Science Foundation(Grant No.2020TQ0042)the Beijing Natural Science Foundation(Grant No.L212008).
文摘Multibody musculoskeletal modeling of human gait has been proved helpful in investigating the pathology of musculoskeletal disorders.However,conventional inverse dynamics methods rely on external force sensors and cannot capture the nonlinear muscle behaviors.Meanwhile,the forward dynamics approach is computationally demanding and only suited for relatively simple tasks.This study proposed an integrated simulation methodology to fulfill the requirements of estimating foot-ground reaction force,tendon elasticity,and muscle recruitment optimization.A hybrid motion capture system,which combines the marker-based infrared device and markerless tracking through deep convolutional neural networks,was developed to track lower limb movements.The foot-ground reaction forces were determined by a contact model for soft materials,and its parameters were estimated using a two-step optimization method.The muscle recruitment problem was first resolved via a static optimization algorithm,and the obtained muscle activations were used as initial values for further simulation.A torque tracking procedure was then performed by minimizing the errors of joint torques calculated by musculotendon equilibrium equations and inverse dynamics.The proposed approach was validated against the electromyography measurements of a healthy subject during gait.The simulation framework provides a robust way of predicting joint torques,musculotendon forces,and muscle activations,which can be beneficial for understanding the biomechanics of normal and pathological gait.
