An Opticalmechanics Framework for Dynamic Estimation of Multibody Systems

2026-06-08

AI Summary

This study addresses the limitations of traditional human dynamics analysis, which relies on contact-based force/torque sensors and controlled environments, rendering it unsuitable for non-contact scenarios. To overcome this, the authors propose an optics-mechanics integrated framework for simultaneous kinematic and dynamic estimation. By formulating a constrained multibody dynamics model, the method leverages vision-based kinematic measurements as non-contact inputs and employs a genetic algorithm to optimize joint torque identification. This approach represents the first demonstration of non-contact dynamic parameter estimation for multibody systems using only visual data and a mechanical model, eliminating dependence on force sensors. Experimental validation on an air-bearing platform shows a mean absolute error of 0.46 Nm in wrist joint torque estimation and a forward-predicted angular velocity error as low as 0.006 rad/s.

Abstract

Conventional dynamics analysis of the human body is often constrained by the need for contact force and torque sensors and controlled laboratory environments. To address this issue, this study proposes an opticalmechanics kinematic-dynamic integrated estimation framework for multibody systems. Specifically, a constrained multibody model is established to describe the system dynamics, while image-measured kinematic quantities are used as non contact inputs for dynamic estimation. The unknown joint torque is then identified through a genetic-algorithm based optimization by minimizing the discrepancy between model-predicted and image-measured kinematic quan tities. Experimental validation on an air-bearing platform showed that the wrist joint torque estimated from image data achieved a mean absolute error of 0.46 Nm compared with sensor measurements. In the forward prediction test, the model-predicted angular velocity achieved a mean absolute error of 0.006 rad/s relative to the image-measured results. This study demonstrates the potential of combining image measurement and mechanical modeling for non-contact dynamic estimation in scenarios where direct force and torque measurement is difficult.

Problem

• multibody dynamics
• contactless measurement
• joint torque estimation
• optical measurement
• dynamic estimation

Innovation

• opticalmechanics
• multibody dynamics
• non-contact estimation
• genetic algorithm optimization
• image-based kinematics