StableLev: Data-Driven Stability Enhancement for Multi-Particle Acoustic Levitation
Lei Gao, Giorgos Christopoulos, Prateek Mittal, Ryuji Hirayama, Sriram Subramanian
Abstract
Acoustic levitation is an emerging technique that has found application in contactless assembly and dynamic displays. It uses precise phase control in an ultrasound transducer array to manage the positions and movements of multiple particles. Yet, maintaining stable mid-air particles is challenging, with unexpected drops disrupting the intended motion and position. Here, we present StableLev, a data-driven pipeline for the detection and amendment of instabilities in multi-particle levitation. We first curate a hybrid levitation dataset, blending optimized simulations with labels based on actual trajectory outcomes. We then design an AutoEncoder to detect anomalies in the simulated data, correlating closely with observed particle drops. Finally, we reconstruct the acoustic field at anomaly regions to improve particle stability and experimentally demonstrate successful dynamic levitation for trajectories within our dataset. Our work provides new insights into multi-particle levitation and enhances its robustness, which will be valuable in a wide range of applications.