Purpose: In cardiac electrophysiology, a long and flexible catheter is delivered to a cardiac chamber for the treatment of arrhythmias. Although several robot-assisted platforms have been commercialized, the disorientation in tele-operation is still not well solved. We propose a validation platform for robot-assisted cardiac EP catheterization, integrating a customized MR Safe robot, a standard clinically used EP catheter, and a human-robot interface. Both model-based and model-free control methods are implemented in the platform for quantitative evaluation and comparison.
Methods: The model-based and model-free control methods were validated by subject test (ten participants), in which the subjects have to perform a simulated radiofrequency ablation task using both methods. A virtual endoscopic view of the catheter is also provided to enhance hand-to-eye coordination. Assessment indices for targeting accuracy and efficiency were acquired for the evaluation.
Results: (1) Accuracy: The average distance measured from catheter tip to the closest lesion target during ablation of model-free method was 19.1% shorter than that of model-based control. (2) Efficiency: The model-free control reduced the total missed targets by 35.8% and the maximum continuously missed targets by 46.2%, both indices corresponded to a low p value ([Formula: see text]).
Conclusion: The model-free method performed better in terms of both accuracy and efficiency, indicating the model-free control could adapt to soft interaction with environment, as compared with the model-based control that does not consider contacts.
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http://dx.doi.org/10.1007/s11548-018-1757-z | DOI Listing |
Front Robot AI
January 2025
Research Institute of Electrical Communication, Tohoku University, Sendai, Japan.
This paper explores the applicability of bicycle-inspired balance control in a quadruped robot model. Bicycles maintain stability and change direction by intuitively steering the handle, which induces yaw motion in the body frame and generates an inertial effect to support balance. Inspired by this balancing strategy, we implemented a similar mechanism in a quadruped robot model, introducing a yaw trunk joint analogous to a bicycle's steering handle.
View Article and Find Full Text PDFChaos
January 2025
Complex Systems Group, Department of Mathematics and Statistics, The University of Western Australia, Crawley, Western Australia 6009, Australia.
We propose a universal method based on deep reinforcement learning (specifically, soft actor-critic) to control the chimera state in the coupled oscillators. The policy for control is learned by maximizing the expectation of the cumulative reward in the reinforcement learning framework. With the aid of the local order parameter, we design a class of reward functions for controlling the chimera state, specifically confining the spatial position of coherent and incoherent domains to any desired lateral position of oscillators.
View Article and Find Full Text PDFNPP Digit Psychiatry Neurosci
January 2025
Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY USA.
Sci Rep
January 2025
Multidisciplinary Center for Infrastructure Engineering, Shenyang University of Technology, Shenyang, 110870, China.
The current research introduces a model-free ultra-local model (MFULM) controller that utilizes the multi-agent on-policy reinforcement learning (MAOPRL) technique for remotely regulating blood pressure through precise drug dosing in a closed-loop system. Within the closed-loop system, there exists a MFULM controller, an observer, and an intelligent MAOPRL algorithm. Initially, a flexible MFULM controller is created to make adjustments to blood pressure and medication dosages.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Electrical Engineering, Chosun University, Gwangju, 61452, South Korea.
The study presents an intelligent, model-free current control strategy that eliminates the need for explicit plant models while efficiently reducing the effect of plant parameter perturbation. By employing a data-driven approach with fewer input features, the proposed scheme reduces the computational burden during training while maintaining high control performance. Unlike conventional model predictive current control (MPCC), which is computationally expensive because of solving optimization problems at each sample time, and requires precise plant models, the proposed method enhances system performance by addressing plant model discrepancies through data-driven techniques.
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