论文标题
自主机器人手术的审议：处理解剖不确定性的框架
Deliberation in autonomous robotic surgery: a framework for handling anatomical uncertainty
论文作者
论文摘要
自主机器人手术需要审议，即计划和执行适应不确定和动态环境的任务的能力。手术结构域的不确定性主要与有关患者特异性解剖学特征的部分术前知识有关。在本文中，我们介绍了一个基于逻辑的框架，用于具有监控和学习的审议功能的手术任务。机器人辅助手术（DEFRAS）的审议框架估计了术前患者特异性计划，并执行它，同时连续测量从生物力学前术前模型获得的施加力。监视模块将该模型与从传感器重建的实际情况进行了比较。如果有显着的不匹配，则调用学习模块以更新模型，从而改善了施加力的估计。在模拟和真实环境中，通过执行软组织缩回的DA Vinci研究套件在模拟环境和真实环境中进行了验证。与最先进的工作相比，任务的成功率得到提高，同时最大程度地减少了与组织的相互作用以防止无意损害。
Autonomous robotic surgery requires deliberation, i.e. the ability to plan and execute a task adapting to uncertain and dynamic environments. Uncertainty in the surgical domain is mainly related to the partial pre-operative knowledge about patient-specific anatomical properties. In this paper, we introduce a logic-based framework for surgical tasks with deliberative functions of monitoring and learning. The DEliberative Framework for Robot-Assisted Surgery (DEFRAS) estimates a pre-operative patient-specific plan, and executes it while continuously measuring the applied force obtained from a biomechanical pre-operative model. Monitoring module compares this model with the actual situation reconstructed from sensors. In case of significant mismatch, the learning module is invoked to update the model, thus improving the estimate of the exerted force. DEFRAS is validated both in simulated and real environment with da Vinci Research Kit executing soft tissue retraction. Compared with state-of-the art related works, the success rate of the task is improved while minimizing the interaction with the tissue to prevent unintentional damage.
