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In this paper, we present a novel and generic data-driven method toservo-control the 3-D shape of continuum and soft robots embedded with fiberBragg grating (FBG) sensors. Developments of 3-D shape perception and controltechnologies are crucial for continuum robots to perform the tasks autonomouslyin surgical interventions. However, owing to the nonlinear properties ofcontinuum robots, one main difficulty lies in the modeling of them, especiallyfor soft robots with variable stiffness. To address this problem, we propose aversatile learning-based adaptive controller by leveraging FBG shape feedbackthat can online estimate the unknown model of continuum robot againstunexpected disturbances and exhibit an adaptive behavior to the unmodeledsystem without priori data exploration. Based on a new composite adaptationalgorithm, the asymptotic convergences of the closed-loop system with learningparameters have been proven by Lyapunov theory. To validate the proposedmethod, we present a comprehensive experimental study by using two continuumrobots both integrated with multi-core FBGs, including a robotic-assistedcolonoscope and multi-section extensible soft manipulators. The resultsdemonstrate the feasibility, adaptability, and superiority of our controller invarious unstructured environments as well as phantom experiments.

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