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From Teleoperation to Autonomous Robot-assisted Microsurgery: A Survey

Abstract

Robot-assisted microsurgery (RAMS) has many benefits compared to traditional microsurgery. Microsurgical platforms with advanced control strategies, high-quality micro-imaging modalities and micro-sensing systems are worth developing to further enhance the clinical outcomes of RAMS. Within only a few decades, microsurgical robotics has evolved into a rapidly developing research field with increasing attention all over the world. Despite the appreciated benefits, significant challenges remain to be solved. In this review paper, the emerging concepts and achievements of RAMS will be presented. We introduce the development tendency of RAMS from teleoperation to autonomous systems. We highlight the upcoming new research opportunities that require joint efforts from both clinicians and engineers to pursue further outcomes for RAMS in years to come.

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Acknowledgements

This work was supported by Royal Society Research, UK (No. RGSR1221 122).

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Correspondence to Chenguang Yang.

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Colored figures are available in the online version at https://link.springer.com/journal/11633

Dandan Zhang received the Ph. D. degree in medical robotics from Computing Department, Imperial College London, UK in 2021. She is currently an honorable researcher at Department of Electrical and Electronic Engineering, Imperial College London, UK. She has been working for Program Grant “Microrobotics for Surgery” between 2018–2022, funded by the Engineering and Physical Sciences Research Council, UK. She is also a lecturer with Department of Engineering Mathematics, University of Bristol, UK. She has been working on micro-robotics, medical robotics, and domestic robotics in the past 3 years and she has published more than 30 papers as the first author or the corresponding author.

Her research interests include robot learning, human-robot shared control and microrobotics.

Weiyong Si received the M. Sc. degree in aerospace engineering from the Beijing Institute of Technology, China in 2018. He is currently a Ph. D. degree candidate in robotics at Bristol Robotics Laboratory and University of the West of England, UK.

His research interests include robot skill learning, teleoperation and robot control.

Wen Fan received the B. Sc. degree in automation from Hefei University of Technology, China in 2020, and the M. Sc. degrees in advanced control and system engineering from University of Manchester, UK in 2021. Currently, he is a research assistant at Intelligent Robotics Group, Department of Mathematics Engineering, University of Bristol, UK.

His research interests include tactile robotics, robot learning, and XAI.

Yuan Guan received the B. Eng degree in engineering from University of Bristol, UK in 2018, and the M. Sc degree in robotics from University College London, UK in 2019. He is currently a Ph. D. degree candidate in robotics at Bristol Robotics Laboratory, University of the West of England, UK.

His research interests include robot learning control and surgical robot.

Chenguang Yang received the Ph. D. degree in control engineering from National University of Singapore, Singapore in 2010. He received the postdoctoral training in human robotics from Imperial College London, London, UK. He was awarded UK EPSRC UKRI Innovation Fellowship and individual EU Marie Curie International Incoming Fellowship. As the lead author, he won the IEEE Transactions on Robotics Best Paper Award (2012) and IEEE Transactions on Neural Networks and Learning Systems Outstanding Paper Award (2022). He is a Co-chair of IEEE Technical Committee on Collaborative Automation for Flexible Manufacturing (CAFM) and a Co-chair of IEEE Technical Committee on Bio-mechatronics and Bio-robotics Systems (B2S).

His research interest include human robot interaction and intelligent system design.

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Zhang, D., Si, W., Fan, W. et al. From Teleoperation to Autonomous Robot-assisted Microsurgery: A Survey. Mach. Intell. Res. 19, 288–306 (2022). https://doi.org/10.1007/s11633-022-1332-5

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Keywords

  • Robot-assisted microsurgery (RAMS)
  • imaging and sensing
  • teleoperation
  • cooperative control
  • robot learning