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首页> 外文期刊>Mechanical Sciences >Laparoscope arm automatic positioning for robot-assisted surgery based on reinforcement learning
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Laparoscope arm automatic positioning for robot-assisted surgery based on reinforcement learning

机译:腹腔镜臂自动定位基于强化学习的机器人辅助手术

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Compared with the traditional laparoscopic surgery, the preoperative planning of robot-assisted laparoscopic surgery is more complex and essential. Through the analysis of the surgical procedures and surgical environment, the laparoscope arm preoperative planning algorithm based on the artificial pneumoperitoneum model, lesion parametrization model is proposed, which ensures that the laparoscope arm satisfies both the distance principle and the direction principle. The algorithm is divided into two parts, including the optimum incision and the optimum angle of laparoscope entry, which makes the laparoscope provide a reasonable initial visual field. A set of parameters based on the actual situation is given to illustrate the algorithm flow in detail. The preoperative planning algorithm offers significant improvements in planning time and quality for robot-assisted laparoscopic surgery. The improved method which combines the preoperative planning algorithm with deep deterministic policy gradient algorithm is applied to laparoscope arm automatic positioning for the robot-assisted laparoscopic surgery. It takes a fixed-point position and lesion parameters as input, and outputs the optimum incision, the optimum angle and motor movements without kinematics. The proposed algorithm is verified through simulations with a virtual environment built by pyglet. The results validate the correctness, feasibility, and robustness of this approach.
机译:与传统的腹腔镜手术相比,机器人辅助腹腔镜手术的术前规划更复杂,必不​​可少。通过分析外科手术和外科环境,提出了基于人工肺术模型的腹腔镜臂术前规划算法,提出了病变参数模型,这确保了探头臂满足距离原理和方向原理。该算法分为两部分,包括最佳切口和腹腔镜进入的最佳角度,这使得腹腔镜提供合理的初始视野。基于实际情况的一组参数被给出详细说明算法流程。术前规划算法在机器人辅助腹腔镜手术中规划时间和质量的显着改进。将具有深度确定性政策梯度算法结合的改进方法应用于机器人辅助腹腔镜手术的腹腔镜臂自动定位。它需要一个固定点位置和病变参数作为输入,输出最佳切口,最佳角度和电动机运动,没有运动学。通过使用Pyglet构建的虚拟环境来验证所提出的算法。结果验证了这种方法的正确性,可行性和鲁棒性。

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