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11.10.2019 | Original Article | Ausgabe 1/2020 Open Access

International Journal of Computer Assisted Radiology and Surgery 1/2020

A microsurgical robot research platform for robot-assisted microsurgery research and training

Zeitschrift:
International Journal of Computer Assisted Radiology and Surgery > Ausgabe 1/2020
Autoren:
Dandan Zhang, Junhong Chen, Wei Li, Daniel Bautista Salinas, Guang-Zhong Yang
Wichtige Hinweise
Dandan Zhang and Junhong Chen contributed to this paper equally.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Abstract

Purpose

Ocular surgery, ear, nose and throat surgery and neurosurgery are typical types of microsurgery. A versatile training platform can assist microsurgical skills development and accelerate the uptake of robot-assisted microsurgery (RAMS). However, the currently available platforms are mainly designed for macro-scale minimally invasive surgery. There is a need to develop a dedicated microsurgical robot research platform for both research and clinical training.

Methods

A microsurgical robot research platform (MRRP) is introduced in this paper. The hardware system includes a slave robot with bimanual manipulators, two master controllers and a vision system. It is flexible to support multiple microsurgical tools. The software architecture is developed based on the robot operating system, which is extensible at high-level control. The selection of master–slave mapping strategy was explored, while comparisons were made between different interfaces.

Results

Experimental verification was conducted based on two microsurgical tasks for training evaluation, i.e. trajectory following and targeting. User study results indicated that the proposed hybrid interface is more effective than the traditional approach in terms of frequency of clutching, task completion time and ease of control.

Conclusion

Results indicated that the MRRP can be utilized for microsurgical skills training, since motion kinematic data and vision data can provide objective means of verification and scoring. The proposed system can further be used for verifying high-level control algorithms and task automation for RAMS research.

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