arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2012.12411cs.RO

Sensing and Reconstruction of 3D Deformation on Pneumatic Soft Robots

Delft University of Technology(代尔夫特理工大学);The Chinese University of Hong Kong(香港中文大学);The University of Manchester(曼彻斯特大学)

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

英文摘要:

Real-time proprioception is a challenging problem for soft robots, which have almost 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 paper to sense and reconstruct 3D 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 3D shape of a soft robot can be accurately reconstructed from signals obtained from multiple sensors. We demonstrate the effectiveness of this approach on two designs of soft robots -- 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 3D deformation from the sensor signals. The sensing and shape prediction pipeline can run at 50Hz in real-time on a consumer-level device.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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