Passively Addressed Robotic Morphing Surface (PARMS) based on machine learning
Abstract
Reconfigurable morphing surfaces provide new opportunities for advanced human-machine interfaces and bio-inspired robotics. Morphing into arbitrary surfaces on demand requires a device with a sufficiently large number of actuators and an inverse control strategy that can calculate the actuator stimulation necessary to achieve a target surface. The programmability of a morphing surface can be improved by increasing the number of independent actuators, but this increases the complexity of the control system. Thus, developing compact and efficient control interfaces and control algorithms is a crucial knowledge gap for the adoption of morphing surfaces in broad applications. In this work, we describe a passively addressed robotic morphing surface (PARMS) composed of matrix-arranged ionic actuators. To reduce the complexity of the physical control interface, we introduce passive matrix addressing. Matrix addressing allows the control of N2 independent actuators using only 2N control inputs, which is significantly lower than N2 control inputs required for traditional direct addressing. Our control algorithm is based on machine learning using finite element simulations as the training data. This machine-learning approach allows both forward and inverse control with high precision in real time. Inverse control demonstrations show that the PARMS can dynamically morph into target surfaces on demand. These innovations in actuator matrix control may enable future implementation of PARMS in wearables, haptics, and augmented reality/virtual reality (AR/VR).
Methods
The dataset include the point cloud data of every experiment in forward and inverse control and the pre-trained ML model based on simulation data. Also, it includes the raw data in single strip and system characterization.
The point cloud data in .txt file can be processed by either Matlab or Python. The pre-trained ML model (they are .m files but not for Matlab) can be used by Python directly to generate the input voltage array (inverse control) or z-displacement array (forward control)
The raw data of single strip actuator and system characterization are in .txt and .csv files. They can be reproduced by Matlab.
Usage notes
The .m files in ML data are pre-trained ML model which should be used by Python, not Matlab. The other data files are in .txt or .csv format. Either Matlab or Python can open them.