Sensing and Reconstruction of 3D Deformation on Pneumatic Soft Robots

Rob B.N. Scharff, Guoxin Fang, Yingjun Tian, Jun Wu, J. M.P. Geraedts, Charlie C.L. Wang

Research output: Contribution to journalArticleScientificpeer-review

15 Citations (Scopus)


Real-time proprioception is a challenging problem for soft robots, which have virtually infinite degrees of freedom in body deformation. When multiple actuators are used, it becomes more difficult as deformation can also occur on actuators caused by interaction between each other. To tackle this problem, we present a method in this article to sense and reconstruct 3-D deformation on pneumatic soft robots by first integrating multiple low-cost sensors inside the chambers of pneumatic actuators and then using machine learning to convert the captured signals into shape parameters of soft robots. An exterior motion capture system is employed to generate the datasets for both training and testing. With the help of good shape parameterization, the 3-D shape of a soft robot can be accurately reconstructed from signals obtained from multiple sensors. We demonstrate the effectiveness of this approach on two soft robot designs - a robotic joint and a deformable membrane. After parameterizing the deformation of these soft robots into compact shape parameters, we can effectively train the neural networks to reconstruct the 3-D deformation from the sensor signals. The sensing and shape prediction pipeline can run at 50 Hz in real time on a consumer-level device.

Original languageEnglish
Article number9426391
Pages (from-to)1877-1885
Number of pages9
JournalIEEE/ASME Transactions on Mechatronics
Issue number4
Publication statusPublished - 2021


  • 3D Deformation
  • Pneumatic Actuators
  • Proprioception
  • Soft Robotics

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