Data-oriented Inverse Kinematics Applied to Soft Robots With Fiducial Markers for Shape Feedback

  • Carlos A. C. Da Purificação SENAI CIMATEC
  • Taniel S. Franklin SENAI CIMATEC
  • Victor S. Matos SENAI CIMATEC
  • Oberdan R. Pinheiro SENAI CIMATEC
  • Lucas C. Silva SENAI CIMATEC

Resumo


Soft manipulators offer many advantages when operating in environments with humans and difficult-to-reach areas. Their high dimensionality introduces modeling difficulties to get an inverse kinematic model. This paper investigates data-oriented inverse kinematics models in a soft robot prototype using fiducial markers to get shape information. A missing values imputation flow is proposed to deal with non-detected ArUco Markers. The results demonstrate the potential of visual feedback combined with machine learning to assist the manipulator estimate control actions to reach a desired shape. Three neural network topologies are evaluated, among which are LSTM, MLP, and Transformer, with the last one returning the best performance.
Palavras-chave: soft robots, robot manipulators, machine learning, artificial intelligence
Publicado
09/10/2023
PURIFICAÇÃO, Carlos A. C. Da; FRANKLIN, Taniel S.; MATOS, Victor S.; PINHEIRO, Oberdan R.; SILVA, Lucas C.. Data-oriented Inverse Kinematics Applied to Soft Robots With Fiducial Markers for Shape Feedback. In: SIMPÓSIO BRASILEIRO DE ROBÓTICA E SIMPÓSIO LATINO AMERICANO DE ROBÓTICA (SBR/LARS), 15. , 2023, Salvador/BA. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 182-187.