Real-time Acoustic Holography with Physics-based Deep Learning for Acoustic Robotic Manipulation

Abstract

Acoustic holography is a newly emerging and promising technique to dynamically generate arbitrary desired holographic acoustic field in 3D space for contactless robotic manipulation. The latest technology supporting complex dynamic holographic acoustic field reconstruction is through phased transducer array (PTA), where the phase profile of emitted acoustic wave from discrete transducers is controlled independently by sophisticated circuits to modulate the acoustic interference field. While the forward kinematics of a phased array based robotic manipulation system is simple and straightforward, the inverse kinematics of the required holographic acoustic field is mathematically non-linear and unsolvable, which substantially limits the application of dynamic holographic acoustic field for robot manipulation. In this work, we propose a physics-based deep learning framework for this phase retrieval inverse kinematics problem so that the target complex hologram could be reconstructed precisely with average MAE of 0.022 and in real time with prediction time of 47 milliseconds on GPU. The accuracy and real time of the proposed method for dynamic holographic acoustic field reconstruction from PTA are demonstrated experimentally.