A solenoid injector based drop-on-demand system for generating large droplets.

This paper proposes a drop-on-demand (DOD) system that can produce single droplets of highly repeatable size in the order of 2 mm. This system utilizes an on-the-shelf solenoid injector used in automotive applications. The design methodology is explained along with the necessary measurements and numerical simulations of droplet generation. The invention consists of a solenoid injector that produces monodisperse single or in-series droplets with the help of a developed pulse width modulated signal generator. Mass per injection is measured over a range of supply pressures and injection durations to find the operation window to generate 2 mm droplets. Later, various nozzle geometries are designed and tested by flow simulations. The contracting nozzle is found suitable for generating single droplets, so the design is implemented at the tip of the solenoid injector. The effects of different opening times, pressures, and nozzle's orifice diameters were tested to observe the operating window of the newly designed DOD system and the repeatability of generated droplets by utilizing a coherent circular Hough transform image processing algorithm to measure droplet sizes. The standard deviation of measured diameters is less than 5% of the mean droplet diameter, which is in the range of 1.68-2.07 mm. Next, the voltage and current signals are measured per injection, and exact instants for the initiation and ending for both opening and closing are determined to construct transient mass flow rate functions for flow simulations in which the dependence of droplet formation on the speed of closing is revealed. The numerical and experimental results indicate the repeatability and consistency of the invention.

A Soft+Rigid Hybrid Exoskeleton Concept in Scissors-Pendulum Mode: A Suit for Human State Sensing and an Exoskeleton for Assistance.

In this paper, we present a novel concept that can enable the human aware control of exoskeletons through the integration of a soft suit and a robotic exoskeleton. Unlike the state-of-the-art exoskeleton controllers which mostly rely on lumped human-robot models, the proposed concept makes use of the independent state measurements concerning the human user and the robot. The ability to observe the human state independently is the key factor in this approach. In order to realize such a system from the hardware point of view, we propose a system integration frame that combines a soft suit for human state measurement and a rigid exoskeleton for human assistance. We identify the technological requirements that are necessary for the realization of such a system with a particular emphasis on soft suit integration. We also propose a template model, named scissor pendulum, that may encapsulate the dominant dynamics of the human-robot combined model to synthesize a controller for human state regulation. A series of simulation experiments were conducted to check the controller performance. As a result, satisfactory human state regulation was attained, adequately confirming that the proposed system could potentially improve exoskeleton-aided applications.

Towards active tracking of beating heart motion in the presence of arrhythmia for robotic assisted beating heart surgery.

In robotic assisted beating heart surgery, the control architecture for heart motion tracking has stringent requirements in terms of bandwidth of the motion that needs to be tracked. In order to achieve sufficient tracking accuracy, feed-forward control algorithms, which rely on estimations of upcoming heart motion, have been proposed in the literature. However, performance of these feed-forward motion control algorithms under heart rhythm variations is an important concern. In their past work, the authors have demonstrated the effectiveness of a receding horizon model predictive control-based algorithm, which used generalized adaptive predictors, under constant and slowly varying heart rate conditions. This paper extends these studies to the case when the heart motion statistics change abruptly and significantly, such as during arrhythmias. A feasibility study is carried out to assess the motion tracking capabilities of the adaptive algorithms in the occurrence of arrhythmia during beating heart surgery. Specifically, the tracking performance of the algorithms is evaluated on prerecorded motion data, which is collected in vivo and includes heart rhythm irregularities. The algorithms are tested using both simulations and bench experiments on a three degree-of-freedom robotic test bed. They are also compared with a position-plus-derivative controller as well as a receding horizon model predictive controller that employs an extended Kalman filter algorithm for predicting future heart motion.

An active brace for controlled transdermal drug delivery for adjustable physical therapy.

This study presents an active brace which is a cost efficient precision-controlled advanced therapy medicinal product for time and rate controlled transdermal drug delivery (TDD) through the use of drug containing nanoparticles and electronics. The active brace is designed to adjust the pressure at the contact area where the medication is applied. The drug is contained in the nanoparticles produced and takes effect when the nanoparticles burst under pressure. The brace is designed to be compact and wearable which can be preprogrammed by a specialist to continue treatment sessions outside the medical facilities providing convenience and comfort to the patient.

Heart Motion Prediction Based on Adaptive Estimation Algorithms for Robotic Assisted Beating Heart Surgery.

Robotic assisted beating heart surgery aims to allow surgeons to operate on a beating heart without stabilizers as if the heart is stationary. The robot actively cancels heart motion by closely following a point of interest (POI) on the heart surface-a process called Active Relative Motion Canceling (ARMC). Due to the high bandwidth of the POI motion, it is necessary to supply the controller with an estimate of the immediate future of the POI motion over a prediction horizon in order to achieve sufficient tracking accuracy. In this paper, two least-square based prediction algorithms, using an adaptive filter to generate future position estimates, are implemented and studied. The first method assumes a linear system relation between the consecutive samples in the prediction horizon. On the contrary, the second method performs this parametrization independently for each point over the whole the horizon. The effects of predictor parameters and variations in heart rate on tracking performance are studied with constant and varying heart rate data. The predictors are evaluated using a 3 degrees of freedom test-bed and prerecorded in-vivo motion data. Then, the one-step prediction and tracking performances of the presented approaches are compared with an Extended Kalman Filter predictor. Finally, the essential features of the proposed prediction algorithms are summarized.

Design of a small animal biopsy robot.

Small animals are widely used in biomedical research studies. They have compact anatomy and small organs. Therefore it is difficult to perceive tumors or cells and perform biopsies manually. Robotics technology offers a convenient and reliable solution for accurate needle insertion. In this paper, a novel 5 degrees of freedom (DOF) robot design for inserting needles into small animal subjects is proposed. The design has a compact size, is light weight, and has high resolution. Parallel mechanisms are used in the design for stable and reliable operation. The proposed robot has two gimbal joints that carry the needle mechanism. The robot can realize dexterous alignment of the needle before insertion.

Model based control algorithms for robotic assisted beating heart surgery.

Robotics technology promises an enhanced way of performing off-pump coronary artery bypass graft (CABG) surgery. In the robotic-assisted CABG surgery, surgeon performs the operation with intelligent robotic instruments controlled through teleoperation that replace conventional surgical tools. The robotic tools actively cancel the relative motion between the surgical instruments and the point-of-interest on the beating heart, in contrast to traditional off-pump CABG where the heart is passively constrained to dampen the beating motion. As a result, the surgeon operates on the heart as if it were stationary. This algorithm is called active relative motion canceling (ARMC). In this paper, the use of biological signals to achieve better motion canceling in the model-based intelligent ARMC algorithm is presented. Also, integration of arrythmia detection and handling with the ARMC algorithm is proposed in order to provide safety over the system. Finally, tracking results of combined respiratory motion and heartbeat motion on a 3-DOF robotic test-bed system are reported.