Deliberation in autonomous robotic surgery: a framework for handling anatomical uncertainty

Abstract

Autonomous robotic surgery requires deliberation, i.e. the ability to plan and execute a task adapting to uncer-tain 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 DE-liberative 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.