In this paper, we validate the effectiveness of the optimal planning algorithms we have developed for devising surgical plans for Percutaneous Nephrolithotomy (PCNL) using patient-specific Concentric-Tube Robots (CTRs). To do so, we built a life-sized phantom model of the right hemithorax, replicating the anatomy of a patient who suffered from kidney stone and underwent conventional PCNL. Two-dimensional CT scans of the phantom model and its 3D reconstruction enabled the creation of a surgical plan using our planning algorithms based on a puncture into the mid-pole of the kidney. This was compared with two other percutaneous tracts involving punctures into the lower and upper calyces for comparison. The optimal mid-pole plan achieved 84% stone coverage, significantly outperforming the lower pole (58 %) and upper pole (45 %) plans. These results validate the effectiveness of the algorithms and align with simulation-based findings from previous studies, which reported an average volume coverage of 81.6±19.6 % in clinical cases.

Electroadhesive clutches have attracted a great deal of interest in the last decade as semi-active actuators for human-robot interaction due to their lightweight, low power consumption, and tunable high-torque output capability. However, because of the complexity of their dynamics, in most cases, they are utilized in an ON/OFF -control strategy. In this regard, the non-autonomous (time-dependent) degradation of electroadhesive behavior is an inherent challenge that injects unpredictability and uncertainty into the behavior of this family of semi-active clutches. We propose a novel approach to preventing degradation of electroadhesion using a segmented electrode design that modulates the electrical field on the dielectric surface while using a direct current signal and securing low power consumption. This paper, for the first time, presents an optimization process based on a novel analytic model of the proposed actuator. It also develops a data-driven model augmentation using a hybrid shallow learning approach composed of a long short-term memory (LSTM) architecture which is combined with the analytical model. The performance of the proposed semi-active clutch and the data-driven hybrid model is experimentally validated in this paper.

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.

The time-domain passivity approach has been proposed in the literature in a variety of formats to guarantee the stability of teleoperation leader-follower systems. The conventional use of the proposed technique utilizes the control effort at the follower side as the force feedback to be sent back to the user at the leader’s side. However, this has resulted in transparency problems, especially when the follower dynamics are not negligible. On the other hand, four-channel and three-channel Lawrence architectures have been investigated widely in the literature to maximize the transparency of the system when, in most advanced cases, stability is guaranteed using wave-variables. However, wave-variables are historically known for their transparency deterioration problems. In this paper, we propose a two-layer approach taking advantage of the fusion of (a) a more optimal derivation of Lawrence telerobotic architecture (utilizing only two channels), and (b) a two-port time-domain passivity stabilizer while comparing the performance with a one-port passivity stabilizer. The two-channel derivation of the Lawrence architecture allows for implementing a two-port time domain passivity approach, which is investigated in this paper. The performance of this is compared systematically through a multi-objective approach by analyzing dissipated energy besides force and velocity errors for a wide range of time delays and frequencies of excitation. The paper gives a comprehensive view of the efficacy of two-port versus one-port time-domain passivity control when combined with the two-channel derivation of Lawrence architecture.