Learning-Based Modeling of Endovascular Navigation for Collaborative Robotic Catheterization
Despite rapid growth of robot assisted catheterization in recent years, most current platforms are based on master-slave designs with limited operator-robot collaborative control and automation. Under this setup, information concerning subject specific behavior and context-driven manoeuvre is not re-utilized for subsequent intervention. For endovascular catheterization, the robot itself is designed with little consideration of underlying skills and associated motion patterns. This paper proposes a learning-based approach for generating optimum motion trajectories from multiple demonstrations of a catheterization task such that it can be used for automating catheter motion within a collaborative setting. Motion models are generated from experienced manipulation of a catheterization procedure and replicated using a robotic catheter driver to assist inexperienced operators. Catheter tip motions of the automated approach are compared against the manual training sets for validating the proposed framework. The results show significant improvements in the quality of catheterization, which facilitate the design of hands-on collaborative robots that make full use of the natural skills of the operators.
KeywordsGaussian Mixture Model Dynamic Time Warping Innominate Artery Smooth Trajectory Multiple Demonstration
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