Towards optimal control of concentric tube robots in stereotactic neurosurgery

Flaßkamp, Kathrin; Worthmann, Karl; Mühlenhoff, Julian; Greiner-Petter, Christoph; Büskens, Christof; Oertel, Joachim; Keiner, Doerthe; Sattel, Thomas

doi:10.1080/13873954.2019.1690004

Cited by 8 publications

(1 citation statement)

References 33 publications

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“…In particular, CTCRs have been proposed as steerable surgical needles to access deep-sited medical targets with minimum damage to surrounding tissue through keyhole body openings [1][2][3]. For achieving high target precision, the combined planning problem of design and actuation has to be carried out, that is, determining geometrical parameters of the tubes as well as actuation parameters for translation and rotation by avoiding given obstacles [4,5]. Therefore, the planning's overall accuracy heavily relies on the accuracy of the used physical model.…”

Section: Introductionmentioning

confidence: 99%

Data augmentation for design of concentric tube continuum robots by generative adversarial networks

Hoffmann,

Gulakala,

Mühlenhoff

et al. 2023

Proc Appl Math and Mech

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Concentric tube continuum robots are a promising type of robot for various medical applications. Their application in neurosurgery poses challenging requirements for design and control that can be addressed by physics‐informed data‐based approaches. A prerequisite to data‐based modeling is an informative, rich data set. However, limited access to experimental data raises interest in partially or entirely synthetic data sets. In this contribution, we study the application of generative adversarial networks (GANs) for data augmentation in a data‐based design process of such robots. We propose a GAN framework suitable for curve‐fitting to generate synthetic trajectories of robots along with their corresponding control parameters. Our evaluation shows that the GANs can efficiently produce meaningful synthetic trajectories and control parameter pairs that show a good agreement with simulated trajectories.

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