Autonomy in parts of robot-assisted surgery is essential to reduce surgeons' cognitive load and eventually improve the overall surgical outcome. A key requirement to ensure safety in an Autonomous Robotic Surgical System (ARSS) lies in the generation of interpretable plans that rely on expert knowledge. Moreover, the ARSS must be able to reason on the dynamic and unpredictable anatomical environment, and quickly adapt the surgical plan in case of unexpected situations. In this paper, we present the first cognitive modular framework for the autonomous planning and execution of surgical tasks in deformable anatomical environments. Our framework integrates a logic module for task-level interpretable reasoning, a physics-based simulation that complements data from real sensors, and a situation awareness module for context interpretation. The framework performance is evaluated on simulated soft tissue retraction, a common surgical task to remove the tissue hiding a region of interest. Results show that the framework has the adaptability required to successfully accomplish the task, handling dynamic environmental conditions and possible failures, while guaranteeing the computational efficiency required in a real surgical scenario. The framework is made publicly available.
翻译:机器人辅助外科手术的某些部分的自主性对于减少外科医生的认知负荷并最终改善总体外科手术结果至关重要。确保自主机器人外科手术系统安全的一个关键要求在于生成依赖专家知识的可解释计划。此外,外科外科手术系统必须能够根据动态和不可预测的解剖环境进行解释,并在出现意外情况时迅速调整外科手术计划。在本文件中,我们介绍了在可变解剖环境中自主规划和执行外科手术任务的第一个认知模块框架。我们的框架包含一个任务级可解释推理逻辑模块,一个基于物理的模拟,以补充来自真实传感器的数据,以及一个背景解释对情况有认识的模块。框架的性能评价是模拟软组织收回,这是清除隐藏在人们感兴趣的地区的组织的共同外科任务。结果显示,框架具有必要的适应性,以成功完成任务,处理动态环境条件和可能的失败,同时保证在真实外科情况下所需的计算效率。框架可以公开提供。