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Title: Modeling Kinematic Uncertainty of Tendon-Driven Continuum Robots via Mixture Density Networks
Tendon-driven continuum robot kinematic models are frequently computationally expensive, inaccurate due to unmodeled effects, or both. In particular, unmodeled effects produce uncertainties that arise during the robot’s operation that lead to variability in the resulting geometry. We propose a novel solution to these issues through the development of a Gaussian mixture kinematic model. We train a mixture density network to output a Gaussian mixture model representation of the robot geometry given the current tendon displacements. This model computes a probability distribution that is more representative of the true distribution of geometries at a given configuration than a model that outputs a single geometry, while also reducing the computation time. We demonstrate one use of this model through a trajectory optimization method that explicitly reasons about the  more » « less
Award ID(s):
2133027
PAR ID:
10547469
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
International Symposium on Medical Robotics (ISMR)
Date Published:
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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