Real-Time Acoustic Holography With Physics-Based Deep Learning for Robotic Manipulation

Acoustic holography (AH) is a promising technique for precise noncontact micro-nano robotic manipulation. It encodes a three-dimensional (3D) acoustic field acting as a virtual end-effector into a two-dimensional (2D) hologram, whereby the desired acoustic field reconstruction is made possible. Most traditional methods to implement AH, such as 3D printed holographic lens and phased array of transducers (PAT), have limitations of dynamic and dexterous manipulation. Furthermore, existing iterative optimization algorithms to calculate 2D holograms have inadequate accuracy and real-time performance. To address these issues, this paper proposes a physics-based deep learning method with a novel training framework for phase-only hologram (POH) calculation enabling further pushing forward the PAT-based AH for noncontact robotic manipulation. By implementing independent control of each channel on PAT referring real-time calculated POH by a well-trained network, the desired acoustic field can be reconstructed in real-time with high fidelity. The results both on a simulated dataset and a real dataset demonstrate that our method supports accurate and dynamic reconstruction of desired acoustic field with distinct morphologies, with an average reconstruction error of 0.085 and average POH computing time of 47 milliseconds on GPU. Indeed, this work shows the future potential of AH in the field of noninvasive medical therapy, exogenous material delivery, and miniaturized industrial assembly. Note to Practitioners-This paper addresses the challenge of noncontact micro-nano robotic manipulation by PAT-based AH, an intriguing technique in bioengineering, micro-assembly, and material characterization. However, existing approaches have limited precision and real-time performance. To overcome these limitations, this paper proposes a physics-based deep learning method with a novel training framework. Our method achieves excellent accuracy and real-time performance, enabling efficient reconstruction of various complicated acoustic field morphologies for precise and dynamic acoustic manipulation. Experimental results demonstrate its high manipulation flexibility due to the independent modulation of each channel of PAT and real-time precise control due to the ultrafast calculation of the proposed deep learning method, though the method has not yet been deployed into an acoustic manipulation system and tested in practice. Future research will focus on designing physical experiments for further evaluation. Overall, the proposed method provides a novel and promising basis for desired acoustic field generation.