Deliberation in autonomous robotic surgery: a framework for handling anatomical uncertainty
arXiv:2203.05438 · doi:10.1109/ICRA46639.2022.9811820
Abstract
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.
arXiv comment2022 International Conference on Robotics and Automation
References in corpus (6)
- Accelerating Surgical Robotics Research: A Review of 10 Years With the da Vinci Research Kit
- ASP-Core-2 Input Language Format
- Autonomous Tissue Manipulation via Surgical Robot Using Learning Based Model Predictive Control
- Overcoming Some Drawbacks of Dynamic Movement Primitives
- Improving rigid 3D calibration for robotic surgery
- Leveraging Vision and Kinematics Data to Improve Realism of Biomechanic Soft-tissue Simulation for Robotic Surgery