Pub Date : 2024-01-02DOI: 10.1146/annurev-control-090623-115925
G. Adam, Mokrane Boudaoud, Valentin Reynaud, J. Agnus, D. Cappelleri, Cédric Clévy
Considering microbotics, microforce sensing, their working environment, and their control architecture together, microrobotic force-sensing systems provide the potential to outperform traditional stand-alone approaches. Microrobotics is a unique way for humans to control interactions between a robot and micrometer-size samples by enabling the control of speeds, dynamics, approach angles, and localization of the contact in a highly versatile manner. Many highly integrated microforce sensors attempt to measure forces occurring during these interactions. However, they are highly difficult to predict because the forces strongly depend on many environmental and system parameters. This article discusses state-of-the-art microrobotic systems for microforce sensing, considering all of these factors. It starts by presenting the basic principles of microrobotic microforce sensing, robotics, and control. It then discusses the importance of microforce sensor calibration and active microforce-sensing techniques. Finally, it provides an overview of microrobotic microforce-sensing systems and applications, including both tethered and untethered microrobotic approaches. Expected final online publication date for the Annual Review of Control, Robotics, and Autonomous Systems, Volume 7 is May 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
{"title":"An Overview of Microrobotic Systems for Microforce Sensing","authors":"G. Adam, Mokrane Boudaoud, Valentin Reynaud, J. Agnus, D. Cappelleri, Cédric Clévy","doi":"10.1146/annurev-control-090623-115925","DOIUrl":"https://doi.org/10.1146/annurev-control-090623-115925","url":null,"abstract":"Considering microbotics, microforce sensing, their working environment, and their control architecture together, microrobotic force-sensing systems provide the potential to outperform traditional stand-alone approaches. Microrobotics is a unique way for humans to control interactions between a robot and micrometer-size samples by enabling the control of speeds, dynamics, approach angles, and localization of the contact in a highly versatile manner. Many highly integrated microforce sensors attempt to measure forces occurring during these interactions. However, they are highly difficult to predict because the forces strongly depend on many environmental and system parameters. This article discusses state-of-the-art microrobotic systems for microforce sensing, considering all of these factors. It starts by presenting the basic principles of microrobotic microforce sensing, robotics, and control. It then discusses the importance of microforce sensor calibration and active microforce-sensing techniques. Finally, it provides an overview of microrobotic microforce-sensing systems and applications, including both tethered and untethered microrobotic approaches. Expected final online publication date for the Annual Review of Control, Robotics, and Autonomous Systems, Volume 7 is May 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.","PeriodicalId":511357,"journal":{"name":"Annual Review of Control, Robotics, and Autonomous Systems","volume":"39 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139390604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-02DOI: 10.1146/annurev-control-091523-100042
Yuanwei Bi, Zhongliang Jiang, Felix Duelmer, Dianye Huang, N. Navab
This article reviews recent advances in intelligent robotic ultrasound imaging systems. We begin by presenting the commonly employed robotic mechanisms and control techniques in robotic ultrasound imaging, along with their clinical applications. Subsequently, we focus on the deployment of machine learning techniques in the development of robotic sonographers, emphasizing crucial developments aimed at enhancing the intelligence of these systems. The methods for achieving autonomous action reasoning are categorized into two sets of approaches: those relying on implicit environmental data interpretation and those using explicit interpretation. Throughout this exploration, we also discuss practical challenges, including those related to the scarcity of medical data, the need for a deeper understanding of the physical aspects involved, and effective data representation approaches. We conclude by highlighting the open problems in the field and analyzing different possible perspectives on how the community could move forward in this research area. Expected final online publication date for the Annual Review of Control, Robotics, and Autonomous Systems, Volume 7 is May 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
{"title":"Machine Learning in Robotic Ultrasound Imaging: Challenges and Perspectives","authors":"Yuanwei Bi, Zhongliang Jiang, Felix Duelmer, Dianye Huang, N. Navab","doi":"10.1146/annurev-control-091523-100042","DOIUrl":"https://doi.org/10.1146/annurev-control-091523-100042","url":null,"abstract":"This article reviews recent advances in intelligent robotic ultrasound imaging systems. We begin by presenting the commonly employed robotic mechanisms and control techniques in robotic ultrasound imaging, along with their clinical applications. Subsequently, we focus on the deployment of machine learning techniques in the development of robotic sonographers, emphasizing crucial developments aimed at enhancing the intelligence of these systems. The methods for achieving autonomous action reasoning are categorized into two sets of approaches: those relying on implicit environmental data interpretation and those using explicit interpretation. Throughout this exploration, we also discuss practical challenges, including those related to the scarcity of medical data, the need for a deeper understanding of the physical aspects involved, and effective data representation approaches. We conclude by highlighting the open problems in the field and analyzing different possible perspectives on how the community could move forward in this research area. Expected final online publication date for the Annual Review of Control, Robotics, and Autonomous Systems, Volume 7 is May 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.","PeriodicalId":511357,"journal":{"name":"Annual Review of Control, Robotics, and Autonomous Systems","volume":"118 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139391440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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