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|Title:||DEEP REINFORCEMENT LEARNING AND ITS APPLICATION FOR FLEXIBLE NEEDLE INSERTION||Authors:||LEE YONGGU||Keywords:||Deep reinforcement learning, Flexible needle insertion, Path planning, Deep learning in robotics and automation, AI based methods||Issue Date:||24-Jul-2019||Citation:||LEE YONGGU (2019-07-24). DEEP REINFORCEMENT LEARNING AND ITS APPLICATION FOR FLEXIBLE NEEDLE INSERTION. ScholarBank@NUS Repository.||Abstract:||Flexible needle insertion with bevel tips is becoming a preferred method for approaching targets in the human body in the least invasive manner. However, to successfully implement needle insertion, surgeons require prolonged training processes and long-term experience to develop essential handling skills. This thesis presents a new path planning approach with Deep Reinforcement Learning (DRL) to implement automatic needle insertion using a surgical robot. In this thesis, Deep Q-Network (DQN) algorithm and Deep Deterministic Policy Gradient (DDPG) algorithm are utilized to learn the control policy for flexible needle steering with needle-tissue interaction. Our proposed algorithms are effective and time efficient. This study shown that deep reinforcement learning can be used to develop accurate path planning algorithm in a complex environment.||URI:||https://scholarbank.nus.edu.sg/handle/10635/162436|
|Appears in Collections:||Master's Theses (Open)|
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