Optimal Handover of Surgical Needle Using Reinforcement Learning for Suturing Automation
- Title
- Optimal Handover of Surgical Needle Using Reinforcement Learning for Suturing Automation
- Author(s)
- Hakyoon Lee
- DGIST Authors
- Hakyoon Lee ; Minho Hwang ; Sang Hyun Park
- Advisor
- 황민호
- Co-Advisor(s)
- Sang Hyun Park
- Issued Date
- 2024
- Awarded Date
- 2024-02-01
- Type
- Thesis
- Description
- Surgery Automation; Suturing Automation; Reinforcement Learning; Deep Q-learning; Autonomous Regrasping; Visual servo control; Robot calibration
- Table Of Contents
-
Ⅰ. Introduction 1
1.1 The Necessity of Suturing Automation 1
1.2 Handover Automation Methods 1
1.3.1 Testing in a Real-World Environment 1
1.3.2 Testing in a Real-World Environment 2
1.3.3 Testing in a Real-World Environment · 2
Ⅱ. Surgical robot system 5
2.1 robot system component 5
2.2 Mechanical design 5
2.3 Remote Center of Motion mechanism 8
2.4 Remote Kinematics · 8
Ⅲ. Calibration 11
3.1 RGBD camera calibration · 11
3.2 Robot position control error calibration · 16
Ⅳ. Reinforcement Learning algorithm 17
4.1 Deep Q-Learning algorithm 17
4.2 State definition 20
4.3 Action definition 21
4.4 Reward definition 21
4.5 Model experiment · 22
Ⅴ. Real robot test 26
5.1.1 Task space control 26
5.2.1 Visual Servo control 29
5.2.2 Needle pose estimation 30
5.2.3 Needle grasping using RGBD camera 32
5.2.4 Experimental result · 32
VI. Conclusion · 34
- URI
-
http://hdl.handle.net/20.500.11750/48072
http://dgist.dcollection.net/common/orgView/200000730097
- Degree
- Master
- Publisher
- DGIST
- Related Researcher
-
-
Hwang, Minho
- Research Interests Robotics and Control; Automation and Learning; Surgical robotics; Mechanism Design; Computer Assisted Surgery; Autonomous Robot; Machine Learning
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- Files in This Item:
-
There are no files associated with this item.
- Appears in Collections:
- Department of Robotics and Mechatronics Engineering Theses Master
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