Statistical Modeling on Motion Trajectories for Robotic Laparoscopic Surgery

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Statistical Modeling on Motion Trajectories for Robotic Laparoscopic Surgery
Title:
Statistical Modeling on Motion Trajectories for Robotic Laparoscopic Surgery
Journal Title:
The 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’17)
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Publication Date:
11 July 2017
Citation:
T. Yang, W.M. Huang, K.K. Toe, Statistical Modeling on Motion Trajectories for Robotic Laparoscopic Surgery, The 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’17), p4347-4350 ,11- 15 July 2017, Jeju, Korea
Abstract:
Learning by demonstration enables a robot to learn and perform tasks from kinesthetic demonstrations. Gaussian mixture method with constraints is applied in this work to model the motion using its trajectories and enable a robot to learn motion skills for a simple surgical task with specific requirement. Tissue dividing experiments are demonstrated on a robotic surgical simulation platform to collect motion trajectories. The demonstrations are modelled using Gaussian Mixture Model. Constraints are also imposed onto the motion model to suit the specific requirements for carrying out the surgical task on a virtual patient. The robot is demonstrated to be able to learn the surgical skills with the statistical model and execute it to complete a virtual surgical task.
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PublisherCopyrights
Funding Info:
SERC102-148-0009 and BMRC-EDB JCO DP grants IAF311022
Description:
(c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
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