Spatial Shape Estimation of a Tendon-Driven Continuum Robotic Arm Using a Vision-Based Algorithm

Pedari, Yasaman; Parvaresh, Aida; Moosavian, S. Ali A.

doi:10.1109/icrom48714.2019.9071908

Cited by 3 publications

(2 citation statements)

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“…Conventional approaches often involve the attachment of markers, such as polystyrene balls, chessboards, adhesives etc. [32], [8], [13], [11], [8], or the usage of paint [5] or LEDs [33], to facilitate the measurement of predefined points along the backbone. The manual addition of markers, though, introduces external influence and may impact the robot's behavior during operation.…”

Section: A Shape Estimation Of Continuum Robotsmentioning

confidence: 99%

“…In the work of Hannan et al [34], a trunk-type robot's shape is reconstructed in 2D through the fitting of circular arcs to the separating disks of the robot. Similarly, Pedari et al [33] model the positions of LEDs on a tendon-driven continuum robot using circular arcs. Camarillo et al [35] employ a shapefrom-silhouette technique, where points from a 3D voxel grid are projected onto images, isolating those that correspond to the continuum robot.…”

Section: A Shape Estimation Of Continuum Robotsmentioning

confidence: 99%

See 1 more Smart Citation

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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