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Artificial Neural Network Based Kinematics: Case Study on Robotic Surgery

  • Ahmed R. J. Almusawi
  • L. C. DülgerEmail author
  • S. Kapucu
Conference paper
Part of the Mechanisms and Machine Science book series (Mechan. Machine Science, volume 73)

Abstract

This study presents a novel controller design for robot-assisted surgery based on Artificial Neural Network (ANN) architecture. The motion of surgical robot is constrained by the kinematics of remote center of motion (RCM). A new ANN design for inverse kinematics of RCM is proposed. ANN compared with classical ANN design. The input pattern of new ANN has included feedback of previous joint angles of robotic arm as well as the position and orientation of the tool tip. A six DOF robotic arm with a tool prototype used to demonstrate a surgical robot. The experimental results proved applicability and efficiency of NN in robotics assisted minimally invasive surgery (RAMIS).

Keywords

Artificial neural network (ANN) robot assisted surgery (RAS) Remote center of motion (RCM) robotic assisted minimally invasive surgery (RAMIS) 

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Notes

Acknowledgment

Scientific Research Projects Governing Unit (BAPYB) supports this study. It is performed in Mechatronics Research Lab. at Gaziantep University. We would like to thank them for their support. The authors are thankful to Prof. Dr. Maruf Şanlı (Faculty of Medicine, Gaziantep University) for his valuable suggestions during this study.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ahmed R. J. Almusawi
    • 1
  • L. C. Dülger
    • 2
    Email author
  • S. Kapucu
    • 3
  1. 1.Mechatronics Eng. DeptUniversity of BaghdadBaghdadIraq
  2. 2.İzmir University of EconomicsİzmirTurkey
  3. 3.Gaziantep UniversityGaziantepTurkey

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