基于FBG的连续体和非结构环境中软体机器人的在线学习及三维形状控制
FBG-Based Online Learning and 3-D Shape Control of Unmodeled Continuum and Soft Robots in Unstructured Environments
摘要：
本文提出了一种新的通用的数据驱动方法来伺服控制嵌入光纤光栅传感器的连续体和软体机器人的3-D形状。三维形状感知和控制技术的发展是连续体机器人自主完成外科手术任务的关键。然而，由于连续体机器人的非线性特性，一个主要的困难在于它们的建模，特别是对于具有变刚度的软体机器人。为了解决这个问题，我们提出了一个通用的基于学习的自适应控制器，利用FBG形状反馈，可以在线估计未知模型的连续体机器人对意外的干扰，并表现出自适应行为的未建模的系统没有先验数据探索。基于一种新的复合自适应算法，利用李雅普诺夫理论证明了具有学习参数的闭环系统的渐近收敛性。为了验证所提出的方法，我们提出了一个全面的实验研究，使用两个连续的机器人都集成了多芯光纤光栅，包括机器人辅助结肠镜和多节可扩展的软操纵器。结果表明，我们的控制器在各种非结构化环境以及幻影实验的可行性，适应性和优越性。
英文摘要：
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.
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