Concentric tube continuum robots

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


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.

Duration: ??? - ???


  • Prof. Dr.-Ing. Thomas Sattel (TU Ilmenau)
  • Daring More Intelligence - Design Assistants in Mechanics and Dynamics




This research is supported by the Deutsche Forschungsgemeinschaft (German Research Foundation) within the Priority Program SPP 2353.