Sensing and Reconstruction of 3D Deformation on Pneumatic Soft Robots
This work provides a real-time 3D deformation sensing and reconstruction method for pneumatic soft robots, which is crucial for their control and autonomy.
This paper addresses the challenge of real-time proprioception in soft robots by integrating multiple low-cost sensors within pneumatic actuators and using machine learning to reconstruct 3D deformation. The method achieves real-time shape prediction at 50Hz on a consumer-level device, demonstrating effectiveness on a robotic joint and a deformable membrane.
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.