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    科学领域:

    • 机器人技术 机器人技术 机器人技术
    • 手术技术 手术技术
    • 人工智能的人工智能

    背景情况:

    • 连续操纵器为神经外科和血管外科手术等复杂手术提供了灵活性.
    • 精确的控制对于机器人辅助内镜手术的成功至关重要.

    研究的目的:

    • 提出和评估一个团队深度Q学习框架 (TDQN) 来控制一个2度自由度 (DoF) 手术连续操纵器.
    • 为了比较TDQN与多代理深度Q学习 (MADQN) 的性能.

    主要方法:

    • 开发一个TDQN框架,其中一对电缆作为代理人共享状态和奖励信息 (集中式学习).
    • 在2-DoF电缆驱动的外科连续操纵器上验证TDQN框架.
    • 使用干扰和无干扰的根平均平方误差 (RMSE) 来定量评估目标准确性.

    主要成果:

    • 与MADQN相比,TDQN实现了明显较低的RMSE.
    • 对于TDQN跟踪的RMSE是0.82mm (有干扰) 和0.16mm (没有干扰).
    • 对于MADQN跟踪的RMSE是1.52mm (有干扰) 和0.98mm (没有干扰).

    结论:

    • 拟议的TDQN在外科连续操纵器中表现出比MADQN更高的目标准确性.
    • TDQN显示出在机器人辅助内镜手术中提高控制精度和机动性的巨大潜力,特别是在受干扰的条件下.