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

基于同心管连续体机器人的个性化医疗手术智能设计辅助

• 批准号: 501928699
• 负责人: Professorin Dr. Kathrin Flaßkamp
• 金额: --
• 依托单位: Professur für Modellierung und Simulation Technischer Systeme
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/501928699?language=en
• 关键词: Intelligent; design; assistance; personalized; medical

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.

连续体机器人在外科手术应用中具有巨大的潜力，因为它们细长的机械手允许在身体深处进行灵活而稳定和准确的操作。然而，它们固有的无限自由度使连续体机器人的设计成为一个挑战。此外，机械操作器参数的设计以及运动规划和控制必须同时考虑，以便找到最佳的外科手术过程本项目开发了一个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。遵循系统工程中的系统设计方法，我们的方法基本上基于1）机器人行为的精确模型和2）数学优化。对于模型生成，物理信息学习用于自动包括实验数据以及专家知识，以克服经典物理模型在精度或计算负载方面的限制。考虑到模块化结构，物理子模型以及不同的基于数据的模型，例如\可以组合通过稀疏回归的神经网络或符号系统表示。这提供了在新应用或任务的情况下快速适应的灵活性。机电设计和运动控制组合问题具有多标准性质，因此将通过多目标优化的元算法来解决。混合物理/基于数据的子模型约束优化问题。此外，容许控制集是通过机器学习的分类技术生成的，以表示稳定性分析的结果。连续体机器人设计问题取决于因此，通过解决代表性病理的多目标优化问题获得的合成数据可以生成一个数据库，在此基础上可以学习替代模型。该AI设计助手可以近似多目标优化的重复解决方案，从而实现与工程师和医生的实时交互。