Neural configuration distance function for continuum robot control
AssigneesToyota Motor Engineering & Manufacturing North America, Inc., Toyota Jidosha Kabushiki Kaisha
InventorsKehan Long, Georgios Fainekos, Bardh Hoxha, Hideki Okamoto
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Abstract
A computer-implemented method of planning motion of a continuum robot in a dynamic environment is provided. The method includes training a neural configuration Euclidean distance function (N-CEDF) using training data and a neural network for learning a geometry of each link of the continuum robot and determining a distance field for each link providing a learned N-CEDF. The learned (N-CEDF) is integrated into a Sampling-Based Path or Mothion Planning algorithm (SBMP) to generate an N-CEDF SBMP framework. The N-CEDF SBMP framework is employed for motion planning of the continuum robot for determining a planned trajectory in the dynamic environment.
Claims20
A computer-implemented method of planning motion of a continuum robot in a dynamic environment, the method comprising:
- training a neural configuration Euclidean distance function (N-CEDF) using training data and a neural network for learning a geometry of each link of the continuum robot and determining a distance field for each link providing a learned N-CEDF;
- integrating the learned (N-CEDF) into a Sampling-Based Path or Motion Planning algorithm (SBMP) saved in memory of the computer to generate an N-CEDF SBMP framework;
- employing the N-CEDF SBMP framework for motion planning of the continuum robot for determining a planned trajectory in the dynamic environment.
A computer-implemented method of planning motion of a continuum robot in a dynamic environment, the method comprising:
- modeling a shape of the continuum robot in a dynamic environment using a neural configuration Euclidean distance function (N-CEDF), where the N-CEDF encoding a geometry of each link of the continuum robot using a neural network;
- integrating the neural configuration Euclidean distance function (N-CEDF) into a Sampling-Based Path or Motion Planning algorithm (SBMP) saved in memory of the computer to generate an N-CEDF SBMP framework;
- and
- determining a planned trajectory for the continuum robot in the dynamic environment using the N-CEDF SBMP framework.
Dependencies are inferred from the claim text.