Browsing by Author "Liu, Peter J."

Now showing 1 - 1 of 1
  • Thumbnail Image
    ItemOpen Access
    Methodology of Robot-Assisted Tool Manipulation for Virtual Reality Based Dissection
    (2019-03-29) Trejo Torres, Fernando Javier; Hu, Yaoping; Sesay, Abu B.; Westwick, David T.; Chan, Sonny; Liu, Peter J.
    Robot-assisted (RA) surgery employs a master-slave system, in which a surgeon's hand manoeuvres the stylus of a hand controller (master) mapped at the operation site to indirectly manipulate a surgical tool attached to the end-effector of a robot (slave). Hence, RA surgery has two drawbacks. Firstly, the transfer of tool-tissue interaction forces to a surgeon is either absent or inaccurate. Secondly, RA surgery incorporates motion coupling (MC) and motion coupling plus orientation match (MC+OM) as indirect modes of tool manipulation, which disregard a pose (position and orientation) match (PM) between the mapped stylus and the tool. This may cause inadvertent tissue trauma during tasks like dissection, which spends ~35.0% of surgery time. Due to the potential of virtual reality (VR) based surgical training, this thesis presents a methodology to address the drawbacks on a VR simulator of soft-tissue dissection. The methodology comprises the formulations and evaluations of an analytic model that estimates dissection forces; and a PM algorithm. The simulator interfaced with the haptic device PHANToM Premium 1.5/6DOF (as a hand controller) to deliver the model forces, and incorporated the kinematics of the device and neuroArm (a neurosurgery robot) for the PM algorithm. The evaluation of the model for estimating dissection forces collected at the tool speeds of 0.10, 1.27, and 2.54 cm/s indicated a force estimation > 80.0%, a computation time < 1.0 ms (the device's update period), and a bandwidth < 30.0 Hz (the device's bandwidth). Moreover, the model lessened cognitive workload for dissections executed at 0.10 cm/s. The evaluation of the PM algorithm revealed a position match < 30.0 µm (the position resolution of the device and neuroArm), an orientation match < 10.0° (to minimize the surgeon's disorientation), and a computation time < 500.0 µs (a half of the device's update period). Additionally, the algorithm became useful to maintain an accurate tool speed and reduce tissue trauma for dissections performed at 0.10 cm/s. The outcomes imply the suitability of the methodology for VR-based RA dissection and their potential to suggest guidelines for VR-based RA dissection training.