2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2018
DOI: 10.1109/iros.2018.8593421
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Trajectory Optimization of Robot-Assisted Endovascular Catheterization with Reinforcement Learning

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Cited by 33 publications
(38 citation statements)
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“…In the future, it is expected that the degree of autonomy in robotic vascular intervention can be increased. W. Chi et al [27] reported vascular intervention path planning using reinforcement learning. Similarly, we plan to apply a reinforced learning algorithm to our vascular intervention robotic system for successful insertion of the catheter into the vascular lines.…”
Section: Discussionmentioning
confidence: 99%
“…In the future, it is expected that the degree of autonomy in robotic vascular intervention can be increased. W. Chi et al [27] reported vascular intervention path planning using reinforcement learning. Similarly, we plan to apply a reinforced learning algorithm to our vascular intervention robotic system for successful insertion of the catheter into the vascular lines.…”
Section: Discussionmentioning
confidence: 99%
“…Chi et al proposed using artificial intelligence to enable the interventional surgery robot to learn from the demonstration of the operation by experts to complete the operation independently or explore autonomously within the vascular model to try to accomplish the surgical goals. The experimental results showed that artificial intelligence could achieve this goal and a more accurate and smoother operation process than manual operation (63)(64)(65)(66).…”
Section: Vascular Interventional Robotic Systemmentioning
confidence: 92%
“…Moreover, RL has been used for rapid trajectory generation for a bimanual needle regrasping task, which is one of the most challenging sub-task of suturing [180] . To avoid collision between surgical tools and delicate tissue regions in the human body, a collision-avoidance path planning algorithm for the laparoscopic robot was designed by combining probabilistic roadmap and RL methods [181] . Dynamic movement primitives (DMP) has been integrated with RL for autonomous cholecystectomy [182] , which can optimize the trajectory of the surgical robot's end-effector and avoid unwanted contact between the catheter tip and the vessel wall.…”
Section: Rl For Autonomous Ramsmentioning
confidence: 99%