Lu Yiang, Chen Wei, Lu Bo, Zhou Jianshu, Chen Zhi, Dou Qi, Liu Yun-Hui
Department of Mechanical and Automation Engineering, T Stone Robotics Institute, The Chinese University of Hong Kong, Shatin, Hong Kong.
The Robotics and Microsystems Center, School of Mechanical and Electric Engineering, Soochow University, Suzhou, China.
Soft Robot. 2024 Apr;11(2):320-337. doi: 10.1089/soro.2022.0158. Epub 2024 Feb 6.
In this article, we present a novel and generic data-driven method to servo-control the 3-D shape of continuum and soft robots based on proprioceptive sensing feedback. Developments of 3-D shape perception and control technologies are crucial for continuum and soft robots to perform 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 shape controller by leveraging proprioception of 3-D configuration from fiber Bragg grating (FBG) sensors, which 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 using two continuum and soft robots both integrated with multicore FBGs, including a robotic-assisted colonoscope and multisection extensible soft manipulators. The results demonstrate the feasibility, adaptability, and superiority of our controller in various unstructured environments, as well as phantom experiments.
在本文中，我们提出了一种新颖的通用数据驱动方法，用于基于本体感知反馈对连续体和软体机器人的三维形状进行伺服控制。三维形状感知和控制技术的发展对于连续体和软体机器人在手术干预中自主执行任务至关重要。然而，由于连续体机器人的非线性特性，一个主要困难在于对它们进行建模，特别是对于具有可变刚度的软体机器人。为了解决这个问题，我们通过利用光纤布拉格光栅（FBG）传感器对三维构型的本体感知，提出了一种通用的基于学习的自适应形状控制器，该控制器可以在线估计连续体机器人的未知模型以应对意外干扰，并在没有先验数据探索的情况下对未建模系统表现出自适应行为。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，我们使用两个均集成了多芯FBG的连续体和软体机器人进行了全面的实验研究，包括一个机器人辅助结肠镜和多节可伸展软体操纵器。结果证明了我们的控制器在各种非结构化环境以及模拟实验中的可行性、适应性和优越性。
