Research in surgical robotics has an established track record at Imperial College, and a number of research and commercial surgical robot platforms have been developed over the years. The Hamlyn Centre is a champion for technological innovation and clinical adoption of robotic, minimally invasive surgery. We work in partnership with major industrial leaders in medical devices and surgical robots, as well as developing our own platforms such as the i-Snake® and Micro-IGES platforms. The Da Vinci surgical robot is used extensively for endoscopic radical prostatectomy, hiatal hernia surgery, and low pelvic and rectal surgery, and in 2003, St Mary’s Hospital carried out its first Totally Endoscopic Robotic Coronary Artery Bypass (TECAB).

The major focus of the Hamlyn Centre is to develop robotic technologies that will transform conventional minimally invasive surgery, explore new ways of empowering robots with human intelligence, and develop[ing miniature 'microbots' with integrated sensing and imaging for targeted therapy and treatment. We work closely with both industrial and academic partners in open platforms such as the DVRK, RAVEN and KUKA. The Centre also has the important mission of driving down costs associated with robotic surgery in order to make the technology more accessible, portable, and affordable. This will allow it to be fully integrated with normal surgical workflows so as to benefit a much wider patient population.

The Hamlyn Centre currently chairs the UK Robotics and Autonomous Systems (UK-RAS) Network. The mission of the Network is to to provide academic leadership in Robotics and Autonomous Systems (RAS), expand collaboration with industry and integrate and coordinate activities across the UK Engineering and Physical Sciences Research Council (EPSRC) funded RAS capital facilities and Centres for Doctoral Training (CDTs).


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  • Journal article
    Zhang D, Liu J, Gao A, Yang G-Zet al., 2020,

    An Ergonomic Shared Workspace Analysis Framework for the Optimal Placement of a Compact Master Control Console

    , IEEE ROBOTICS AND AUTOMATION LETTERS, Vol: 5, Pages: 2995-3002, ISSN: 2377-3766
  • Journal article
    Zhao M, Oude Vrielink TJC, Kogkas A, Runciman M, Elson D, Mylonas Get al., 2020,

    LaryngoTORS: a novel cable-driven parallel robotic system for transoral laser phonosurgery

    , IEEE Robotics and Automation Letters, Vol: 5, Pages: 1516-1523, ISSN: 2377-3766

    Transoral laser phonosurgery is a commonly used surgical procedure in which a laser beam is used to perform incision, ablation or photocoagulation of laryngeal tissues. Two techniques are commonly practiced: free beam and fiber delivery. For free beam delivery, a laser scanner is integrated into a surgical microscope to provide an accurate laser scanning pattern. This approach can only be used under direct line of sight, which may cause increased postoperative pain to the patient and injury, is uncomfortable for the surgeon during prolonged operations, the manipulability is poor and extensive training is required. In contrast, in the fiber delivery technique, a flexible fiber is used to transmit the laser beam and therefore does not require direct line of sight. However, this can only achieve manual level accuracy, repeatability and velocity, and does not allow for pattern scanning. Robotic systems have been developed to overcome the limitations of both techniques. However, these systems offer limited workspace and degrees-of-freedom (DoF), limiting their clinical applicability. This work presents the LaryngoTORS, a robotic system that aims at overcoming the limitations of the two techniques, by using a cable-driven parallel mechanism (CDPM) attached at the end of a curved laryngeal blade for controlling the end tip of the laser fiber. The system allows autonomous generation of scanning patterns or user driven freepath scanning. Path scan validation demonstrated errors as low as 0.054±0.028 mm and high repeatability of 0.027±0.020 mm (6×2 mm arc line). Ex vivo tests on chicken tissue have been carried out. The results show the ability of the system to overcome limitations of current methods with high accuracy and repeatability using the superior fiber delivery approach.

  • Journal article
    Kiziroglou M, Temelkuran B, Yeatman E, Yang GZet al., 2020,

    Micro motion amplification – A Review

    , IEEE Access, Vol: 8, Pages: 64037-34055, ISSN: 2169-3536

    Many motion-active materials have recently emerged, with new methods of integration into actuator components and systems-on-chip. Along with established microprocessors, interconnectivity capabilities and emerging powering methods, they offer a unique opportunity for the development of interactive millimeter and micrometer scale systems with combined sensing and actuating capabilities. The amplification of nanoscale material motion to a functional range is a key requirement for motion interaction and practical applications, including medical micro-robotics, micro-vehicles and micro-motion energy harvesting. Motion amplification concepts include various types of leverage, flextensional mechanisms, unimorphs, micro-walking /micro-motor systems, and structural resonance. A review of the research state-of-art and product availability shows that the available mechanisms offer a motion gain in the range of 10. The limiting factor is the aspect ratio of the moving structure that is achievable in the microscale. Flexures offer high gains because they allow the application of input displacement in the close vicinity of an effective pivotal point. They also involve simple and monolithic fabrication methods allowing combination of multiple amplification stages. Currently, commercially available motion amplifiers can provide strokes as high as 2% of their size. The combination of high-force piezoelectric stacks or unimorph beams with compliant structure optimization methods is expected to make available a new class of high-performance motion translators for microsystems.

  • Journal article
    Zhang D, Liu J, Zhang L, Yang G-Zet al., 2020,

    Hamlyn CRM: a compact master manipulator for surgical robot remote control.

    , Int J Comput Assist Radiol Surg, Vol: 15, Pages: 503-514

    PURPOSE: Compact master manipulators have inherent advantages, since they can have practical deployment within the general surgical environments easily and bring benefits to surgical training. To assess the advantages of compact master manipulators for surgical skills training and the performance of general robot-assisted surgical tasks, Hamlyn Compact Robotic Master (Hamlyn CRM) is built up and evaluated in this paper. METHODS: A compact structure for the master manipulator is proposed. A novel sensing system is designed while stable real-time motion tracking can be realized by fusing the information from multiple sensors. User studies were conducted based on a ring transfer task and a needle passing task to explore a suitable mapping strategy for the compact master manipulator to control a surgical robot remotely. The overall usability of the Hamlyn CRM is verified based on the da Vinci Research Kit (dVRK). The master manipulators of the dVRK control console are used as the reference RESULTS: Motion tracking experiments verified that the proposed system can track the operators' hand motion precisely. As for the master-slave mapping strategy, user studies proved that the combination of the position relative mapping mode and the orientation absolute mapping mode is suitable for Robot-Assisted Minimally Invasive Surgery (RAMIS), while key parameters for mapping are selected. CONCLUSION: Results indicated that the Hamlyn CRM can serve as a compact master manipulator for surgical training and has potential applications for RAMIS.

  • Journal article
    Freer D, Yang G-Z, 2020,

    Data augmentation for self-paced motor imagery classification with C-LSTM.

    , Journal of Neural Engineering, Vol: 17, Pages: 1-15, ISSN: 1741-2552

    OBJECTIVE: Brain Computer Interfaces (BCI) are becoming important tools for assistive technology, particularly through the use of Motor Imagery (MI) for aiding task completion. However, most existing methods of MI classification have been applied in a trial-wise fashion, with window sizes of approximately 2 seconds or more. Application of this type of classifier could cause a delay when switching between MI events. APPROACH: In this study, state-of-the-art classification methods for motor imagery are assessed with considerations for real-time and self-paced control, and a Convolutional Long-Short Term Memory (C-LSTM) network based on Filter Bank Common Spatial Patterns (FBCSP) is proposed. In addition, the effects of several methods of data augmentation on different classifiers are explored. MAIN RESULTS: The results of this study show that the proposed network achieves adequate results in distinguishing between different control classes, but both considered deep learning models are still less reliable than a Riemannian Minimum Distance to the Mean (MDM) classifier. In addition, controlled skewing of the data and the explored data augmentation methods improved the average overall accuracy of the classifiers by 14.0% and 5.3%, respectively. SIGNIFICANCE: This manuscript is among the first to attempt combining convolutional and recurrent neural network layers for the purpose of MI classification, and is also one of the first to provide an in-depth comparison of various data augmentation methods for MI classification. In addition, all of these methods are applied on smaller windows of data and with consideration to ambient data, which provides a more realistic test bed for real-time and self-paced control.

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