Non-contact laser ablation, a precise thermal technique, simultaneously cuts and coagulates tissue without the insertion errors associated with rigid needles. Human organ motions, such as those in the liver, exhibit rhythmic components influenced by respiratory and cardiac cycles, making effective laser energy delivery to target lesions while compensating for tumor motion crucial. This research introduces a data-driven method to derive surrogate models of a soft manipulator. These low-dimensional models offer computational efficiency when integrated into the Model Predictive Control (MPC) framework, while still capturing the manipulator's dynamics with and without control input. Spectral Submanifolds (SSM) theory models the manipulator's autonomous dynamics, acknowledging its tendency to reach equilibrium when external forces are removed. Preliminary results show that the MPC controller using the surrogate model outperforms two other models within the same MPC framework. The data-driven MPC controller also supports a design-agnostic feature, allowing the interchangeability of different soft manipulators within the laser ablation surgery robot system.

翻译：非接触式激光消融作为一种精密热技术，可在避免刚性针具穿刺误差的同时实现对组织的同步切割与凝血。人体器官（如肝脏）运动呈现受呼吸与心动周期影响的节律性特征，因此在补偿肿瘤运动的同时实现激光能量向靶病灶的有效递送至关重要。本研究提出一种数据驱动方法以建立柔性机械臂的代理模型。这些低维模型在集成至模型预测控制（MPC）框架时具有计算高效性，同时仍能准确捕捉机械臂在有/无控制输入状态下的动力学特性。谱子流形（SSM）理论对机械臂自主动力学进行建模，揭示了其在撤除外力时趋于平衡的内在特性。初步结果表明，采用代理模型的MPC控制器在相同MPC框架内优于另外两种对比模型。该数据驱动的MPC控制器还具备设计无关性特征，支持在激光消融手术机器人系统中对不同柔性机械臂进行互换使用。