Intelligent design assistance for personalized medical surgery based on concentric tube continuum robots

连续体机器人由于其细长的机械手可以在人体深处灵活而稳定而精确地进行操作，因此在外科手术中具有巨大的应用潜力。然而，由于其固有的无限自由度，使得连续体机器人的设计成为一个挑战。此外，为了找到适合单个患者的最优手术方式，必须同时考虑机械臂参数的设计和运动规划与控制。本项目开发了一种用于脑深部神经外科手术的同心管连续体机器人的设计辅助工具。遵循系统工程的系统设计方法学，该方法基于1)精确的机器人行为模型和2)数学优化.在模型生成中，使用物理知识学习自动包含实验数据和专家知识，以克服经典物理模型在精度或计算量方面的局限性.考虑到模块化结构，可以将物理子模型和不同的基于数据的模型相结合，例如神经网络或符号系统通过稀疏回归表示.机电一体化设计和运动控制问题是多学科的，因此将通过多目标优化的元启发式方法来解决。基于物理/数据的混合子模型约束优化问题。此外，机器学习中的分类技术生成允许的控制集，以表示稳定性分析的结果。连续统机器人设计问题取决于个体患者的手术需求。因此，解决典型病理的多目标优化问题的合成数据生成了一个数据库，在此数据库上可以学习代理模型。接近多目标优化的重复解，这个人工智能设计助手允许与工程师和医生进行实时交互。

Continuum robots have great potential in surgical applications since their slender-like manipulators allow to operate flexible --yet stable and accurate-- deep within the body.However, their intrinsically given, infinite degree of freedom has made the continuum robot design a challenge.Moreover, the design of mechanical manipulator parameters and motion planning and control have to be considered simultaneously in order to find the optimal surgical procedure for an individual patient.This project develops a design assistant tool for concentric tube continuum robots to be used in deep-brain neurosurgery.Following the systematic design methodology in systems engineering, our approach is fundamentally based on 1) accurate models of the robots behavior and 2) mathematical optimization.For model generation, physics-informed learning is used to automatically include experimental data as well as expert's knowledge to overcome limits of classical physical models in accuracy or computational load.Considering a modularized structure, both physical submodels as well as different data-based models, e.g.\ neural networks or symbolic system representations by sparse regression can be combined. This offers flexibility regarding fast adaption in case of new applications or tasks.The combined mechatronic design and motion control problem is of multicriterial nature and, thus, will be addressed by meta-heuristics of multi-objective optimization.The hybrid physics-/data-based submodels constrain the optimization problem.Additionally, the admissible control set is generated by classification techniques from machine learning in order to represent results from stability analysis.The continuum robot design problem depends on the individual patient's surgery needs.Thus, synthetic data from solving the multi-objective optimization problem for representative pathologies generates a data base on which a surrogate model can be learned.Approximating the repetitive solutions of multi-objective optimization, this AI design assistant allows for real-time interaction with engineers and physicians.