Search

Manual catheter‐based interventions (CBIs) suffer from exposure of the interventionalists to X‐ray, and dependence of their performance on the expertise and fatigue level of the interventionalists. Robot‐assisted catheterization systems (RACS) have been introduced in recent years to improve the efficiency of CBIs; however, using them is still associated with some difficulties such as set‐up dependency to a specific type of intervention instrument, not being portable, and offering limited options of operation modes. The objective of this research is to develop a new RACS to address these shortcomings. We propose Althea II as an improvement for our previously introduced RACS, Althea I. Althea II is designed for both research purposes and clinical applications including catheter‐based cardiovascular interventions. Althea II benefits from a novel structural design leading to a significantly reduced weight and making the device inclusive for a broader range of intervention instruments. Also, a tip detection algorithm is developed and integrated into the graphical user interface (GUI) to enable image‐based navigation, and accordingly, fully automatic navigation. Althea II has improved the outcome of catheter‐based interventions by increased accuracy and precision of the intervention. The system can navigate the catheter tip to a designated target with an accuracy higher than 90% in both velocity and positioning mode. The device is associated with an upgraded GUI equipped with a strong tip detection algorithm with an accuracy of 0.05 mm. Moreover, Althea II gains from a quicker assembly time (20 minutes, which equals five times faster). The independency from specific catheters, several modes of function, an imaged‐based feedback control, portability, and a remote function should allow operation even from beginners and reduce X‐ray exposure. The preliminary research studies verified the accuracy and repeatability of Althea II, demonstrated the feasibility and applicability of using the set‐up in multiple applications, and highlighted the improved set‐up capabilities over the currently available RACS. [ABSTRACT FROM AUTHOR]

Due to their compliance, continuum robots (CRs) hold great potential for many applications. However, despite intensive recent research, their control poses significant challenges. The nonlinear kinematic behavior, limited actuation channels, and physical and environmental constraints typically associated with CRs hinder the development of effective control strategies. In this paper, a visual predictive position control method for tendon-driven continuum robots is proposed. The developed control approach integrates the advantages of image-based visual servoing and model predictive control techniques to enable direct end-point control in the presence of constraints and improve the control robustness to system uncertainties, sensing noise, and modeling errors. Both simulation and experimental results demonstrate the effectiveness of the method. • First model-based VPC method for image-based positioning of CRs incorporating the system and environment constraints. • First derivation of the full model of rotational ablation catheters and its integration into the VPC framework. • Investigating the robustness of the proposed controller to the modeling uncertainty, set-up performance error, and noise of the sensory module. • Experimental verification of the proposed VPC in 3-D. [ABSTRACT FROM AUTHOR]