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Communication Dans Un Congrès Année : 2022

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

Résumé

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-theart related works, the success rate of the task is improved while minimizing the interaction with the tissue to prevent unintentional damage.
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Dates et versions

hal-03671364 , version 1 (18-05-2022)

Identifiants

  • HAL Id : hal-03671364 , version 1

Citer

Eleonora Tagliabue, Daniele Meli, Diego Dall'Alba, Paolo Fiorini. Deliberation in autonomous robotic surgery: a framework for handling anatomical uncertainty. 2022 IEEE International Conference on Robotics and Automation (ICRA 2022), IEEE, May 2022, Philadelphie, PA, United States. ⟨hal-03671364⟩

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