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
    Liu J, Hu H,

    Jindong Liu, Huosheng Hu Department of Computer Science, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UK Received 2003-11-18 Revised 2004-6-12 Online

  • Journal article
    Freer D, Yang G-Z,

    MIndGrasp: A New Training and Testing Framework for Motor Imagery Based 3-Dimensional Assistive Robotic Control

    With increasing global age and disability assistive robots are becoming morenecessary, and brain computer interfaces (BCI) are often proposed as a solutionto understanding the intent of a disabled person that needs assistance. Mostframeworks for electroencephalography (EEG)-based motor imagery (MI) BCIcontrol rely on the direct control of the robot in Cartesian space. However,for 3-dimensional movement, this requires 6 motor imagery classes, which is adifficult distinction even for more experienced BCI users. In this paper, wepresent a simulated training and testing framework which reduces the number ofmotor imagery classes to 4 while still grasping objects in three-dimensionalspace. This is achieved through semi-autonomous eye-in-hand vision-basedcontrol of the robotic arm, while the user-controlled BCI achieves movement tothe left and right, as well as movement toward and away from the object ofinterest. Additionally, the framework includes a method of training a BCIdirectly on the assistive robotic system, which should be more easilytransferrable to a real-world assistive robot than using a standard trainingprotocol such as Graz-BCI. Presented results do not consider real human EEGdata, but are rather shown as a baseline for comparison with future human dataand other improvements on the system.

  • Journal article
    Davila-Chacon J, Twiefel J, Liu J, Wermter Set al.,

    Behavioural Robotics for Speech Recognition

  • Journal article
    Freer D, Guo Y, Deligianni F, Yang G-Zet al.,

    On-Orbit Operations Simulator for Workload Measurement during Telerobotic Training

    Training for telerobotic systems often makes heavy use of simulatedplatforms, which ensure safe operation during the learning process. Outer spaceis one domain in which such a simulated training platform would be useful, asOn-Orbit Operations (O3) can be costly, inefficient, or even dangerous if notperformed properly. In this paper, we present a new telerobotic trainingsimulator for the Canadarm2 on the International Space Station (ISS), which isable to modulate workload through the addition of confounding factors such aslatency, obstacles, and time pressure. In addition, multimodal physiologicaldata is collected from subjects as they perform a task from the simulator underthese different conditions. As most current workload measures are subjective,we analyse objective measures from the simulator and EEG data that can providea reliable measure. ANOVA of task data revealed which simulator-basedperformance measures could predict the presence of latency and time pressure.Furthermore, EEG classification using a Riemannian classifier andLeave-One-Subject-Out cross-validation showed promising classificationperformance and allowed for comparison of different channel configurations andpreprocessing methods. Additionally, Riemannian distance and beta power of EEGdata were investigated as potential cross-trial and continuous workloadmeasures.

  • Journal article
    Davila-Chacon J, Twiefel J, Liu J, Wermter Set al.,

    Improving Humanoid Robot Speech Recognition with Sound Source Localisation

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