Esfandiari Mojtaba, Du Pengyuan, Wei Haochen, Gehlbach Peter, Munawar Adnan, Kazanzides Peter, Iordachita Iulian
Mojtaba Esfandiari, Pengyuan Du, and Iulian Iordachita are with the Department of Mechanical Engineering and Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD, 21218, USA.
Haochen Wei, Peter Kazanzides, are with the Department of Computer Science and Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD, 21218, USA.
Proc Am Control Conf. 2025 Jul;2025:3341-3347. doi: 10.23919/acc63710.2025.11107661. Epub 2025 Aug 21.
Modeling and controlling cable-driven snake robots is a challenging problem due to nonlinear mechanical properties such as hysteresis, variable stiffness, and unknown friction between the actuation cables and the robot body. This challenge is more significant for snake robots in ophthalmic surgery applications, such as the Improved Integrated Robotic Intraocular Snake (IRIS), given its small size and lack of embedded sensory feedback. Data-driven models take advantage of global function approximations, reducing complicated analytical models' challenge and computational costs. However, their performance might deteriorate in case of new data unseen in the training phase. Therefore, adding an adaptation mechanism might improve these models' performance during snake robots' interactions with unknown environments. In this work, we applied a model predictive path integral (MPPI) controller on a data-driven model of the IRIS based on the Gaussian mixture model (GMM) and Gaussian mixture regression (GMR). To analyze the performance of the MPPI in unseen robot-tissue interaction situations, unknown external disturbances and environmental loads are simulated and added to the GMM-GMR model. These uncertainties of the robot model are then identified online using a radial basis function (RBF) whose weights are updated using an extended Kalman filter (EKF). Simulation results demonstrated the robustness of the optimal control solutions of the MPPI algorithm and its computational superiority over a conventional model predictive control (MPC) algorithm.
由于诸如滞后、可变刚度以及驱动电缆与机器人主体之间未知摩擦力等非线性机械特性，对电缆驱动的蛇形机器人进行建模和控制是一个具有挑战性的问题。对于眼科手术应用中的蛇形机器人，如改进的集成机器人眼内蛇形机器人（IRIS），这一挑战更为显著，因为其尺寸小且缺乏嵌入式传感反馈。数据驱动模型利用全局函数逼近，降低了复杂分析模型的挑战性和计算成本。然而，在训练阶段未见过的新数据情况下，它们的性能可能会下降。因此，添加一种自适应机制可能会在蛇形机器人与未知环境交互期间提高这些模型的性能。在这项工作中，我们基于高斯混合模型（GMM）和高斯混合回归（GMR），将模型预测路径积分（MPPI）控制器应用于IRIS的数据驱动模型。为了分析MPPI在未见过的机器人与组织交互情况下的性能，模拟了未知外部干扰和环境负载并将其添加到GMM - GMR模型中。然后使用径向基函数（RBF）在线识别机器人模型的这些不确定性，其权重使用扩展卡尔曼滤波器（EKF）进行更新。仿真结果证明了MPPI算法最优控制解的鲁棒性及其相对于传统模型预测控制（MPC）算法的计算优势。
