A knowledge-based framework for task automation in surgery
Autor: | Paolo Fiorini, Daniele Meli, Michele Ginesi, Andrea Roberti, Hirenkumar Nakawala |
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Jazyk: | angličtina |
Rok vydání: | 2019 |
Předmět: |
0209 industrial biotechnology
medicine.medical_specialty Computer science media_common.quotation_subject ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology Ontology (information science) Task (project management) 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering medicine Quality (business) Robotic surgery ontology dynamic movement primitives computer.programming_language media_common business.industry Modular design Autonomous robotic surgery ontology dynamic movement primitives Autonomous robotic surgery Planner Automation Surgery Scalability 020201 artificial intelligence & image processing business computer |
Zdroj: | 2019 19th International Conference on Advanced Robotics (ICAR) ICAR |
Popis: | Robotic surgery has significantly improved the quality of surgical procedures. In the past, researches have been focused on automating simple surgical actions. However, there exists no scalable framework for automation in surgery. In this paper, we present a knowledge-based modular framework for the automation of articulated surgical tasks, for example, with multiple coordinated actions. The framework is consisted of ontology, providing entities for surgical automation and rules for task planning, and “dynamic movement primitives” as adaptive motion planner as to replicate the dexterity of surgeons. To validate our framework, we chose a paradigmatic scenario of a peg-and-ring task, a standard training exercise for novice surgeons which presents many challenges of real surgery, e.g. grasping and transferring. Experiments show the validity of the framework and its adaptability to faulty events. The modular architecture is expected to generalize to different tasks and platforms. |
Databáze: | OpenAIRE |
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