Binary Amplitude-Only Hologram Design for Acoustic End-Effector Construction by Physics-Based Deep Learning

Acoustic holography has emerged as a cutting-edge technique for constructing a micro-robot acoustic end-effector for non-contact manipulation. As one of typical implementations of acoustic holography, Binary Amplitude-Only Hologram (BAOH) featured with a simple structure provides an efficient alternative for modulating acoustic fields that support micro- robotic manipulation. In the present study, we propose a deep learning based BAOH generation method for constructing precise and high-resolution end-effector based on acoustic field. Specifically, we model the BAOH generation problem into an optimization framework. The framework combines an acoustic wave propagation model with the deep neural network, in favor of bypassing the laborious collection of labeled data and facilitating the model to learn the inverse mapping. Additionally, to address the issues of gradient invalidation and information loss caused by binarization, the framework uses an adaptive binarization layer consisting of differentiable binarization and adaptive threshold automatically learned during training, which facilitates to realize end-to-end optimization and increase the non-linear capacity of the model. The simulation experiments show that the proposed method is capable to predict BAOH that supports precise, robust, versatile and real-time construction of acoustic end-effector, enjoying broad prospects in various applications related to micro-robotic manipulation.