Real-time Acoustic Holography with Iterative Unsupervised Learning for Acoustic Robotic Manipulation

Phase-only acoustic holography is a fundamental and promising technique for contactless robotic manipulation. Through independently controlling phase-only hologram (POH) of phase array of transducers (PAT) and simultaneously driving each channel by sophisticated circuits, a certain acoustic field is dynamically generated in working medium (e.g., air, water or biological tissues) at certain moment. The phase profile of PAT is required dynamically and precisely as per arbitrary expected acoustic field for the sake of versatile and stable robotic manipulation. However, the most conventional methods rely on iterative optimization algorithms which are inevitably time-consuming and probably non-convergent, moreover hindering versatility and fidelity of acoustic robotic manipulation. To address these issues, this paper reports a real-time phase-only acoustic holography algorithm by virtue of iterative unsupervised learning. Using a physics model to construct two queues, which we refer to as experience pools, data pairs consisting of a target acoustic amplitude hologram in expected acoustic field and corresponding POH of PAT are collected on-the-fly, circumventing costly preparation of annotated dataset in advance. With iterative learning between neural network training and experience pools update, both the solution of objective inverse mapping and the adaptation for arbitrary desired acoustic field are mutually enhanced. The experiments and results validated that the proposed approach surpasses previous algorithms in terms of real time and precision.