Human arm kinematics modeling method based on Gaussian process learning

Abstract

The invention discloses a human arm kinematics modeling method based on Gaussian process learning. The method comprises establishing a seven-degree-of-freedom (DOF) human arm kinematics model, inputting the palm pose of the human arm and the lengths of the upper arm and the lower arm, and outputting the most comfortable arm configuration when the human arm moves. According to the method, an infrared motion capture instrument is used for collecting shoulder joints, elbow joints, wrist joints, palms central pose and the lengths of upper and lower arms when human arms move in a most comfortable manner; a Gaussian process regression method is used for learning a Gaussian process regression model of a palm center pose, upper and lower arm lengths and an arm angle when a human arm moves in a most comfortable mode, and a human arm kinematics model based on Gaussian process learning is established. The method has the advantages that the complex motion mechanism of the human arm does not need to be studied, and compared with traditional methods based on energy optimization, biomechanical models and the like, calculation is simpler; the palm center pose is used as input, the lengths of the upper arm and the lower arm are used as input, and therefore the anthropomorphic arm obtained through the method is more accurate in configuration and higher in universality.

Description

technical field [0001] The invention relates to a kinematics modeling method, in particular to a human arm kinematics modeling method and a humanoid mechanical arm motion control. Background technique [0002] The traditional human arm kinematic modeling method needs to study the human arm kinematic mechanism to obtain the anthropomorphic arm configuration, such as establishing the human arm kinematic model based on the energy optimal and biomechanical model. The main problems of this kind of method are the calculation complexity and calculation accuracy. Low. In recent years, machine learning methods have been widely studied, specifically studying how computers simulate or realize human learning behaviors, and acquire new knowledge or skills to solve complex problems. At present, some studies apply machine learning methods to human arm kinematics modeling, such as using Bayesian networks to establish human arm kinematics models, but this method does not consider the impact...

AI Technical Summary

Problems solved by technology

At present, some studies apply machine learning methods to human arm kinematics modeling, such as using Bayesian networks to establish human arm kinematics models, but this method does not consider the impact of wrist joints on the redundancy of human arms; The network learns the kinematic model of the human arm, and the pose of the end of the palm is used as input, but this method does not consider the influence of arm length

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