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.

中文翻译：

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非结构化环境中连续体和软机器人的自适应在线学习和鲁棒 3D 形状伺服。


在本文中，我们提出了一种新颖且通用的数据驱动方法，用于基于本体感觉传感反馈对连续体和软体机器人的 3D 形状进行伺服控制。 3D 形状感知和控制技术的发展对于连续体和软体机器人在外科手术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，一个主要困难在于它们的建模，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，我们提出了一种基于学习的多功能自适应形状控制器，利用光纤布拉格光栅（FBG）传感器的3D配置的本体感觉，它可以在线估计连续体机器人针对意外干扰的未知模型，并表现出自适应行为到未经先验数据探索的未建模系统。基于一种新的复合自适应算法，利用Lyapunov理论证明了带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，我们使用两个与多核光纤光栅集成的连续体和软机器人进行了全面的实验研究，包括机器人辅助结肠镜和多部分可扩展软机械手。结果证明了我们的控制器在各种非结构化环境以及仿真实验中的可行性、适应性和优越性。