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Title: Hand-eye calibration, constraints and source synchronisation for robotic-assisted minimally invasive surgery
Author: Pachtrachai, Krittin
ISNI:       0000 0004 9359 9648
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2020
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In robotic-assisted minimally invasive surgery (RMIS), the robotic system allows surgeons to remotely control articulated instruments to perform surgical interventions and introduces a potential to implement computer-assisted interventions (CAI). However, the information in the camera must be correctly transformed into the robot coordinate as its movement is controlled by the robot kinematic. Therefore, determining the rigid transformation connecting the coordinates is necessary. Such process is called hand-eye calibration. One of the challenges in solving the hand-eye problem in the RMIS setup is data asynchronicity, which occurs when tracking equipments are integrated into a robotic system and create temporal misalignment. For the calibration itself, noise in the robot and camera motions can be propagated to the calibrated result and as a result of a limited motion range, the error cannot be fully suppressed. Finally, the calibration procedure must be adaptive and simple so a disruption in a surgical workflow is minimal since any change in the setup may require another calibration procedure. We propose solutions to deal with the asynchronicity, noise sensitivity, and a limited motion range. We also propose a potential to use a surgical instrument as the calibration target to reduce the complexity in the calibration procedure. The proposed algorithms are validated through extensive experiments with synthetic and real data from the da Vinci Research Kit and the KUKA robot arms. The calibration performance is compared with existing hand-eye algorithms and it shows promising results. Although the calibration using a surgical instrument as the calibration target still requires a further development, results indicate that the proposed methods increase the calibration performance, and contribute to finding an optimal solution to the hand-eye problem in robotic surgery.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available