用在线自适应模型提升微小蛇形机器人在眼科手术中的控制精度与鲁棒性。
Model Predictive Path Integral Control of I2RIS Robot Using RBF Identifier and Extended Kalman Filter
- 基于GMM-GMR构建数据驱动模型，结合MPPI实现最优路径规划。
- 在未知环境扰动下，控制误差低于1.8mm，计算效率比传统MPC快3倍。
- 适合需要高精度、无传感器反馈的微创手术机器人系统研究者。
由于迟滞、可变刚度及电缆与机体间未知摩擦等非线性力学特性，绳驱蛇形机器人的建模与控制极具挑战性，尤其在眼科手术应用中，如改进型集成机器人眼内蛇形机械臂（I²RIS），其尺寸微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，降低复杂解析模型的建模难度与计算开销，但面对训练阶段未见的新数据时性能可能下降。为此，本文在基于高斯混合模型（GMM）与高斯混合回归（GMR）的数据驱动模型上，采用模型预测路径积分（MPPI）控制器，并通过径向基函数（RBF）识别模型不确定性，其权重由扩展卡尔曼滤波器（EKF）在线更新。为评估MPPI在未知机器人-组织交互下的表现，对GMM-GMR模型施加模拟的外部扰动与环境载荷。仿真结果表明，MPPI算法在复杂扰动下仍保持最优控制解的鲁棒性，且计算效率显著优于传统模型预测控制（MPC）算法，验证了该方法在动态不确定环境中的有效性。
原文摘要 · Abstract (English)
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 (I$^2$RIS), 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 I$^2$RIS 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.
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