AcousNet: A Deep Learning Based Approach to Dynamic 3D Holographic Acoustic Field Generation From Phased Transducer Array

Abstract

Holographic acoustic field has shown great potential for non-contact robotic manipulations of millimeter or sub-millimeter size objects to effectively deliver acoustic power. The latest technology for generating dynamic holographic acoustic field is through phased transducer array, where relative phases of emitted acoustic waves from transducers are independently controlled 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 (i.e., the mapping from a given holographic acoustic field to array phases for control purpose), however, is mathematically non-linear and unsolvable, presenting challenges in developing wider applications of holographic acoustic field for robotic manipulation. Considering, thus far, there are still no effective solutions reported, the authors put intensive efforts to solve this problem using a machine learning approach, namely a deep neural network architecture, which we refer to as AcousNet. Experimental results demonstrate the effectiveness of the proposed method for dynamic holographic acoustic field generation from phased transducer array.