混合密度ネットワークによる腱駆動連続ロボットの運動学的不確かさのモデリング
Modeling Kinematic Uncertainty of Tendon-Driven Continuum Robots via Mixture Density Networks
腱変位からロボット形状のガウス混合分布を出力する混合密度ネットワークを学習し、不確かさを考慮した軌道最適化に応用した。
著者: Jordan Thompson, Brian Y. Cho, Daniel S. Brown, Alan Kuntz
分類: cs.RO
原文アブストラクト
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 workspace uncertainty to minimize the probability of collision.
