FBG-Based Online Learning and 3-D Shape Control of Unmodeled Continuum and Soft Robots in Unstructured Environments

TL;DR

This paper introduces a data-driven, adaptive control method for 3-D shape regulation of continuum and soft robots with FBG sensors, enabling autonomous operation in unstructured environments despite nonlinearities and unknown dynamics.

Contribution

It proposes a novel learning-based adaptive controller leveraging FBG feedback, capable of online model estimation without prior data exploration, validated through extensive experiments.

Findings

Controller demonstrates high adaptability in unstructured environments

Experimental results show superior shape control accuracy

Method effectively handles unmodeled dynamics and disturbances

Abstract

In this paper, we present a novel and generic data-driven method to servo-control the 3-D shape of continuum and soft robots embedded with fiber Bragg grating (FBG) sensors. Developments of 3-D shape perception and control technologies are crucial for continuum robots to perform the tasks autonomously in surgical interventions. However, owing to the nonlinear properties of continuum robots, one main difficulty lies in the modeling of them, especially for soft robots with variable stiffness. To address this problem, we propose a versatile learning-based adaptive controller by leveraging FBG shape feedback that can online estimate the unknown model of continuum robot against unexpected disturbances and exhibit an adaptive behavior to the unmodeled system without priori data exploration. Based on a new composite adaptation algorithm, the asymptotic convergences of the closed-loop system with learning parameters have been proven by Lyapunov theory. To validate the proposed method, we present a comprehensive experimental study by using two continuum robots both integrated with multi-core FBGs, including a robotic-assisted colonoscope and multi-section extensible soft manipulators. The results demonstrate the feasibility, adaptability, and superiority of our controller in various unstructured environments as well as phantom experiments.