Discrete Geometric Modeling and Extended State Estimation of Continuum Robots

2026-06-19

AI Summary

This work addresses the challenges of insufficient modeling accuracy in continuum robot dynamics and the susceptibility of state estimation to model uncertainties and external disturbances. To overcome these issues, the authors propose a fully discrete modeling framework that integrates the geometrically exact beam formulation based on the minimum strain representation with a Lie group variational integrator. Furthermore, an extended Kalman filter–based disturbance observer is designed to simultaneously estimate the system states, model errors, and external disturbances. By preserving the intrinsic geometric structure of the system, the proposed approach significantly enhances both modeling accuracy and robustness. Experimental validation on a physical platform demonstrates that the developed model and observer achieve high precision, computational efficiency, and reliable disturbance estimation under real-world conditions.

Abstract

In this paper, we present a fully discrete approach for the accurate and numerically efficient dynamical modeling and state estimation of continuum robots. The model is based on geometrically exact beams in a minimal, strain-based formulation and derived in the framework of Lie group variational integrators, allowing to preserve important geometric properties that we exploit to achieve high accuracy and numerical efficiency. We then propose a disturbance observer based on an extended Kalman filter formulation that reliably estimates system states as well as model uncertainties and external disturbances. Experiments on a real system validate the accuracy and efficiency of the proposed model and observer.

Problem

• continuum robots
• dynamical modeling
• state estimation
• model uncertainty
• external disturbances

Innovation

• discrete geometric modeling
• Lie group variational integrators
• continuum robots
• extended Kalman filter
• disturbance observer