Tendon-based modelling, estimation and control for a simulated high-DoF anthropomorphic hand model

TL;DR

This paper introduces a computational method to estimate joint angles in tendon-driven robotic hands using tendon measurements, enabling gesture control without direct joint sensors, demonstrated in simulation.

Contribution

It presents a novel kinematic modeling and nonlinear optimization approach for joint estimation and a Jacobian-based PI control scheme for tendon-driven anthropomorphic hands.

Findings

Effective joint angle estimation from tendon data in simulation

Successful gesture tracking without joint encoders

Identified limitations in real-world application

Abstract

Tendon-driven anthropomorphic robotic hands often lack direct joint angle sensing, as the integration of joint encoders can compromise mechanical compactness and dexterity. This paper presents a computational method for estimating joint positions from measured tendon displacements and tensions. An efficient kinematic modeling framework for anthropomorphic hands is first introduced based on the Denavit-Hartenberg convention. Using a simplified tendon model, a system of nonlinear equations relating tendon states to joint positions is derived and solved via a nonlinear optimization approach. The estimated joint angles are then employed for closed-loop control through a Jacobian-based proportional-integral (PI) controller augmented with a feedforward term, enabling gesture tracking without direct joint sensing. The effectiveness and limitations of the proposed estimation and control framework are demonstrated in the MuJoCo simulation environment using the Anatomically Correct Biomechatronic Hand, featuring five degrees of freedom for each long finger and six degrees of freedom for the thumb.