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

文献类型：期刊论文

作者Zhong, Chengxi4; Li, Jiaqi4; Sun, Zhenhuan4; Li, Teng4; Guo, Yao3; Jeong, David C.2; Su, Hu1; Liu, Song4,5
刊名IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
出版日期2023-07-17
页码10
关键词Index Terms-Acoustic holography (AH) inverse kinematics phased array of transducers (PAT) robotic manipulation
ISSN号1545-5955
DOI10.1109/TASE.2023.3292885
通讯作者Su, Hu(hu.su@ia.ac.cn) ; Liu, Song(liusong@shanghaitech.edu.cn)
英文摘要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.
WOS关键词IMAGE
资助项目Shanghai Pujiang Program[21PJ1410500] ; ShanghaiTech Startup Funding
WOS研究方向Automation & Control Systems
语种英语
WOS记录号WOS:001035933400001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构Shanghai Pujiang Program ; ShanghaiTech Startup Funding
源URL[http://ir.ia.ac.cn/handle/173211/53804]  
专题多模态人工智能系统全国重点实验室
通讯作者Su, Hu; Liu, Song
作者单位1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
2.Santa Clara Univ, Dept Commun, Santa Clara, CA 95053 USA
3.Shanghai Jiao Tong Univ, Inst Med Robot, Shanghai 200240, Peoples R China
4.ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai 201210, Peoples R China
5.Shanghai Engn Res Ctr Intelligent Vis & Imaging, Shanghai 201210, Peoples R China
推荐引用方式
GB/T 7714		 Zhong, Chengxi,Li, Jiaqi,Sun, Zhenhuan,et al. Real-Time Acoustic Holography With Physics-Based Deep Learning for Robotic Manipulation[J]. IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING,2023:10.
APA Zhong, Chengxi.,Li, Jiaqi.,Sun, Zhenhuan.,Li, Teng.,Guo, Yao.,...&Liu, Song.(2023).Real-Time Acoustic Holography With Physics-Based Deep Learning for Robotic Manipulation.IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING,10.
MLA Zhong, Chengxi,et al."Real-Time Acoustic Holography With Physics-Based Deep Learning for Robotic Manipulation".IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING (2023):10.

入库方式： OAI收割

来源：自动化研究所

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