In this paper, we present a novel and generic data-driven method to servo-control the 3-D shape of continuum 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 new robust adaptive controller by leveraging FBG shape feedback and neural networks (NNs) that can online estimate the unknown model of continuum robot and accounts for unexpected disturbances together with NN approximation errors, which exhibits 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 NNs 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.
翻译:在本文中,我们提出了一个新型通用数据驱动方法,用于控制嵌入纤维布拉格格仪传感器的三维连续机器人的3D形状。3D形状的视觉和控制技术的发展对于连续机器人在外科手术中自主地执行任务至关重要。然而,由于连续机器人的非线性特性,一个主要困难在于其模型的建模,特别是软机器人的软机器人,其僵硬程度各异。为了解决这个问题,我们提议一个新的强大的适应控制器,利用FBG形状反馈和神经网络(NNS),该控制器可以在线估计连续机器人的未知模型,并记录出乎意料的干扰和NNNN近似误差的账户。3D形状的认知和控制技术的发展对于连续机器人在外科手术干预中自主地执行任务至关重要。但是,由于新的复合适应算法,闭路机器人系统与NNS学习参数的无症状趋同已经得到Lyapunov理论的证明。为了验证拟议的方法,我们提出一个全面的实验研究,使用两个连续机器人机器人的机器人模型,既与多核心FG系统相结合,又能算算算算算出出出出出出出出出出出出意外的意外干扰的干扰机器人的干扰机器人的内空机机机机机机率,包括机器人的机能和机率的机率的机率级的机率级的机率级的机率的机率级的机率。