Navid Feizi

This article presents a dynamic modeling framework for tendon-driven continuum manipulators, with a focus on cardiac catheters. Tendon compliance and tendon–sheath friction produce nonuniform tensions and motion-history-dependent dynamics (hysteresis), which, if neglected, degrade model fidelity and control. We develop a Cosserat-based formulation that integrates tendon compliance and a dynamic LuGre friction law into the governing nonlinear PDEs. This allows geometrically exact spatiotemporal computation of tendon–tension transmission without assuming initial friction states. Experiments on two clinical catheters demonstrate that incorporating friction dynamics reduces distal-tip error from $1.62\pm 1.01$ mm (frictionless) to $0.99\pm 0.65$ mm ($\approx 39\%$ improvement), and achieves $2.31\pm 0.91$ mm mean absolute error for out-of-plane spatial actuation of a four-tendon ICE catheter. Comparative analysis against a Coulomb-based capstan model highlights the significance of dynamic friction, which produces rate-dependent tension transmission and more accurate modeling of hysteresis effects.

This paper presents an error-state Model Predictive Path Integral (MPPI) framework for tendon-driven continuum robots (TDCRs). Tracking-error dynamics are formulated on a Lie group to preserve full pose geometry, yielding precise position–orientation error metrics. A nonlinear Cosserat-rod model with strain parameterization provides a closed-form TDCR dynamics representation and updates in $0.3 \pm \text{0.3}\,\text{ms}$. The model is calibrated via weight-release and actuation experiments on robotic ablation catheters, and its generalized coordinates are estimated through nested optimization. The MPPI controller parallelizes trajectory sampling and evaluation, uses tendon-displacement actuation computed via optimization to eliminate force sensors, and is uncertainty-aware through a simple and efficient exponentially weighted moving-average (EWMA) estimator embedded in the running cost. Control trajectories are sampled around the current best sequence and evaluated with an adaptive cost and exponential weighting to bias low-cost solutions. Experiments comparing conventional model predictive control (MPC), Lie-group MPC, offline Implicit Q-Learning (IQL), and MPPI formulated with Cartesian errors show that our MPPI method achieves the highest accuracy, significantly better computational efficiency than MPC, and better overall accuracy than all baselines. The model further extends naturally to multi-segment TDCRs and can incorporate tendon-actuation friction.

Concentric-tube robots (CTRs) have garnered significant attention in various minimally invasive procedures due to their small size and dexterity. Despite extensive technical advancements in the development of CTRs, there is a lack of design approaches specific to their function as surgical instruments. This study proposes a compact CTR specifically designed for percutaneous nephrolithotomy (PCNL), adaptable for both hand-held operation and mounting on a passive arm. We employ a parallel carriage-based design to reduce the device’s cross-sectional footprint (46 mm diameter, 322 mm length) and localize the center of mass (570 g mass) beneath the grip area, enhancing ergonomic comfort and control. An ergonomic evaluation of the robot during the handling of the robot by expert urologists, as well as non-clinicians, showed better ergonomics than standard hand-held PCNL devices. Additionally, closed-loop position control of the distal end of the CTR was implemented based on resolved-motion rate inverse kinematics. The performance of the robot was empirically validated through experiments on a life-size abdominal phantom. The results showed mean closed-loop position errors of 1.2±0.8 mm for autonomous navigation to 100 target points on the stone, indicating a performance level in line with the specific requirements of PCNL.

Concentric tube robots (CTRs) have garnered significant attention in various minimally invasive procedures due to their small size and dexterity, enabling access to target clinical sites through minimal incisions. Despite extensive technical advancements in the development of CTRs, there is a lack of design approaches specific to their function as surgical instruments. In this study, we propose a compact CTR specifically designed for percutaneous nephrolithotomy (PCNL), adaptable for both hand-held operation and mounting on a passive arm. The proposed robot measures 46 mm in diameter, 322 mm in length, and weighs 570 g. An ergonomic evaluation of the robot through muscle activation analysis during the handling of the robot by expert urologists and non-clinicians showed better ergonomics than standard hand-held PCNL devices. Additionally, closed-loop position control of the distal end of the CTR was implemented based on resolved-motion rate inverse kinematics. The performance of the robot was empirically validated through a life-size abdominal phantom-based experiment in three phases of the procedure that included skin puncturing, autonomous deployment, and navigation to all parts of a renal stone. The results showed mean closed-loop position errors of 1.2±0.8 mm for autonomous navigation to 100 target points on the stone, indicating a performance level in line with the specific requirements of PCNL.

Tendon-driven continuum robots (TDCRs) have received widespread attention in the medical domain due to their slender shape and flexibility. Modeling the dynamics of TDCRs involves continuum mechanics that result in nonlinear and computationally intensive models posing challenges for real-time control. In this work, we propose efficient, and control-oriented modeling of the nonlinear dynamics of TDCRs leveraging the deep Koopman approach. This method transforms the states of the system into an intrinsic nonlinear manifold, where the autonomous dynamics are approximated linearly, and the actuation input enters the system with a bilinear term. The proposed model captures the nonlinearities, including space-dependent variations in the system spectrum. Position control is implemented using a linear quadratic controller, leveraging the linear nature of the Koopman operator. The accuracy of the proposed method is experimentally validated using a dual-tendon robotic steerable catheter, achieving a position tracking error of 1.64 mm (SD = 0.74) for multi-sinusoidal, and 0.60 mm (SD = 0.30) for sinusoidal (0.05 Hz) target trajectories. The results demonstrate the potential for applying the proposed approach for real-time control of a broad range of TDCRs.

The increasing need for sharing workspace and interactive physical tasks between robots and humans has raised concerns regarding the safety of such operations. In this regard, controllable clutches have shown great potential for addressing important safety concerns at the hardware level by separating the high-impedance actuator from the end-effector by providing the power transfer from electromagnetic source to the human. However, the existing clutches suffer from high power consumption and large weight, which make them undesirable from the design point of view. In this article, for the first time, the design and development of a novel, lightweight, and low-power torque-adjustable rotary clutch using electroadhesive materials are presented. The performance of three different pairs of clutch plates is investigated in the context of the smoothness and quality of output torque. The performance degradation issue due to the polarization of the insulator is addressed through the utilization of an alternating current waveform activation signal. Moreover, the effect of the activation frequency on the output torque and power consumption of the clutch is investigated. Finally, a time-dependent model for the output torque of the clutch is presented, and the performance of the clutch was evaluated through experiments, including physical human–robot interaction. The proposed clutch offers a torque-to-power consumption ratio that is six times better than commercial magnetic particle clutches. The proposed clutch presents great potential for developing safe, lightweight, and low-power physical human–robot interaction systems, such as exoskeletons and robotic walkers.

Smart actuators with tunable dynamical characteristics have shown significant potential to improve the performance of physical human-robot interaction systems, such as exoskeletons. In this regard, electroadhesive clutches, which function based on a controllable dry kinetic friction between the discs, have attracted a great deal of interest for semi-passive actuation that has a high force-to-weight ratio and low power consumption. However, the complex dynamical behavior of such an actuator has limited the potential application in dynamic tasks. This paper, for the first time, presents a robust nonlinear control law for torque-adjustable rotary electroadhesive clutches in order to guarantee smooth, agile, and stable behavior in the presence of significant unmodeled dynamics. For this, a nonlinear Lyapunov-redesign approach is proposed based on a partially-identified model, which can guarantee convergence and robustness to unmodeled dynamics. The performance of the proposed algorithm is validated through a comprehensive set of experiments, including active-resistive and coordination-assisted rehabilitation using an elbow exoskeleton designed based on the rotary electroadhesive clutch. The proposed approach shows potential for application in dynamic scenarios such as those involving assistive and wearable robots.

Bilateral telerobotic systems have attracted a great deal of interest during the last two decades. The major challenges in this field are the transparency and stability of remote force rendering, which are affected by network delays causing asynchrony between the actions and the corresponding reactions. In addition, the overactivation of stabilizers further degrades the fidelity of the rendered force field. In this article, a real-time frequency-based delay compensation approach is proposed to maximize transparency while reducing the activation of the stabilization layer. The algorithm uses a regulated bound-limited multiple Fourier linear combiner to extract the dominant frequency of force waves. The estimated weights are used in conjunction with the relatively phase-lead harmonic kernels to reconstruct the signal and generate a compensated wave to reduce the effect of the delay. The reconstructed force will then pass through a modulated time-domain passivity controller to guarantee the stability of the system. We will show that the proposed technique will reduce the force-tracking error by 40% and the activation of the stabilizer by 79%. It is shown, for the first time, that through the utilization of online adaptive frequency-based prediction, the asynchrony between transmitted waves through delayednetworks can be significantly mitigated while stability can be guaranteed with less activation of the stabilization layer.