This paper presents a methodology for optimizing cable anchor points for cable-driven parallel robots (CDPRs) for specific additive manufacturing tasks. Much of a CDPR’s workspace is generally not used for printing tasks. The unused workspace of the CDPR can be sacrificed to gain greater control to fulfill the printing task. In this paper, the CDPR is designed for a specific task to achieve the best printing results. To find the optimum robot size, the stiffness of the end-effector and mean cable tension are analyzed. The end-effector compliance index (ECI) is proposed to assess the stiffness of the end-effector within the workspace. The ECI uses cable directions to determine the compliance of a given robot pose. From simulation results, a relation to get optimum CDPR frame size is achieved for both suspended and constrained type CDPRs. The proposed method can be used to design low-cost cable-driven robots for additive manufacturing.

Exoskeletons are increasingly helping people with different applications. Regardless of what they were built for, exoskeletons have a common discomfort problem from the misalignment of robot and human joints. In this paper, a fixation design for a lower extremity exoskeleton is presented. A method was proposed to determine necessary passive degrees of freedom of the designed brace system and to identify the parameters affecting interaction forces and moments between human and exoskeleton. The proposed method was validated by analyzing the human-machine interface statically and dynamically. The results show that the problem of undesired interaction forces due to misalignment may be solved theoretically with the proposed design.

Percutaneous needle procedures are mostly carried out with the guidance of 2D ultrasound (US) imaging. US images are inherently noisy and their resolutions are low. Hence, target tracking can be challenging. Image based tracking methods can be used to track the needle and the target. This paper proposes visual tracking of multiple moving points, such as biopsy needles and targets, in 2D US images using normalized cross correlation and mutual information similarity functions. Both moving and deformable targets can be tracked. An affine motion model is used for small and moving target tracking and a thin plate spline motion model is used for deformable target tracking. During the tracking, needle and target template images are updated with a template update strategy. Also, tracking outputs of normalized cross correlation and mutual information are fused using the Kalman filter to reduce the tracking error. During the experiments, needle is inserted using a needle insertion robot. 2D US probe is attached to a robotic arm's end effector to servo the probe along the needle insertion path. Proposed needle and target tracking methods were tested with phantoms. Accuracies of the needle tip and moving target tracking methods were measured using an optical tracking system. Experimental results showed that the proposed tracking method could be used to simultaneously track the needle tip and the targets in real-time in 2D US guided percutaneous needle procedures.

Ultrasound (US) is one the most commonly used medical imaging techniques in percutaneous needle procedures. However, US images are inherently noisy and contain excessive number of artifacts. Hence, it is not easy to track the needle tip in the US images during the needle insertions. At this point, image based visual tracking techniques can be used for needle tip tracking. This paper presents a method for visual tracking of biopsy needles in 2D US images using sum of squared differences and sum of conditional variances. Second order Gauss-Newton optimization is used to decrease processing time and make the tracking more robust. The needle template images used in the method are updated with a strategy to prevent needle loss and detection failures during tracking. The paper also explains how to identify needle losses during tracking and how to recover the needle position without using a needle localization algorithm. We demonstrate the precision of the visual needle tip tracking method with experiments under challenging tracking conditions.

Necessity of precise positioning in percutaneous needle operations enables robotic systems to take part in medical procedures. Researches that have been done until today showed robotic systems increase the accuracy of medical interventions. This paper presents the design and modeling of a 5DOF parallel robot that will be used in Ultrasound image guided percutaneous needle interventions. In order to design the robot, workspace and torque analysis were conducted. System identification was performed on robot to understand the system dynamics. System calibration was performed with optical tracking system to increase the accuracy of the system. Gravity compensation and friction modeling were conducted on the robot. Using gravity compensation and friction models, a torque computed controller was implemented to system.

In the percutaneous needle procedures using ultrasound (US) imaging, the needle should be detected precisely to avoid damage to the tissue and to get the samples from the appropriate site. Excessive artifacts and low resolution of the US images make it difficult to detect the needle and its tip. It is possible to enhance the needle image using image processing; and this work proposes a novel needle detection method in 2D US images based on the Gabor filter. This method enhances the needle outline while suppressing the other structures in the image. First, the needle insertion angle is estimated and then the needle trajectory is found with the RANSAC line estimator. The experiments with three different phantoms showed that the algorithm is robust and could work in percutaneous needle procedures using US images.

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. 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-squares-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 three-degree-of-freedom (DOF) 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.

We are developing a personal micronavigation system that uses high-resolution gait-corrected inertial measurement units. The goal of this project is to develop a navigation system that use secondary inertial variables, such as velocity, to enable long-term precise navigation in the absence of Global Positioning System (GPS) and beacon signals. In this scheme, measured zero velocity durations from the ground reaction sensors are used to reset the accumulated integration errors from the accelerometers and gyroscopes in position calculation. We achieved an average position error of 4 meters at the end of half-hour walks.

In biomedical research it is difficult to perceive tumors or cells and perform biopsies manually. Robotics technology can offer a reliable solution for accurate needle insertion. A novel 5 degrees of freedom (DOF) robot for inserting needles into small animal subjects was developed. The robot can realize dexterous alignment of the needle using two parallel mechanisms, and has a syringe mechanism to insert needles to subjects. Operations on small animals require high accuracy positioning during needle insertion. In this paper, kinematic calibration of the 5 DOF robot using an optical tracker as an external sensor is performed to enhance accuracy of the system.

In order to perform coronary artery bypass graft surgery, a stationary heart is necessary. A human cannot achieve manual tracking of the complex heartbeat motion. Robotics technology can overcome such limitations. In the robotic-assisted beating heart surgery, the robot actively cancels heart motion by closely following a point of interest on the heart surface-a process called active relative motion canceling. As a result, surgeon can operate on the beating heart as if it is stationary. In this paper, a generalized estimation algorithm, that uses an adaptive filter to generate future position estimates is studied. The predictor is parameterized on-line and adaptively to minimize the prediction error in the mean-square sense. The predictor is evaluated using a 3-degree- of-freedom test-bed system and prerecorded heart motion data.

In the robotic-assisted off-pump coronary artery bypass graft (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. Measuring the motion of the heart during this operation is an important part of this scheme. In this paper, a novel whisker sensor design to measure the heart motion in three dimensions (3D) is presented. The proposed whisker sensor is a flexible contact sensor. Low stiffness of the sensor prevents damage on the tissue it contacts. This paper explains the design concept, and reports the simulation and measurement results of the prototype whisker position sensor

Robot assisted heart surgery allows surgeons to operate on a heart while it is still beating as if it had been stopped. 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 over a prediction horizon. In this paper, a prediction algorithm, using an adaptive filter to generate future position estimates, is implemented and studied. The effects of predictor parameters on tracking performance are studied. Finally, the predictor is evaluated using a 3 degrees of freedom test-bed and prerecorded heart motion data.

This paper focuses on the development of control algorithms for intelligent robotic tools that assist off-pump coronary artery bypass graft (CABG) surgery. In the robotic-assisted CABG surgery, the surgeon operates on the beating heart using intelligent robotic instruments. Robotic tools actively cancel the relative motion between the surgical instruments and the point of interest on the beating heart, dynamically stabilizing the heart for the operation. This algorithm is called active relative motion canceling (ARMC). Here, a model-based intelligent ARMC algorithm employing biological signals, such as electrocardiogram, to achieve effective motion cancellation is proposed. Finally, experimental results of the algorithm on a 3-degree-of-freedom robotic test-bed system are reported.

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 in place of 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, such as electrocardiogram (ECG), to achieve better motion canceling in the model-based intelligent ARMC algorithm is proposed. An ECG contains records for the electrical activity of the heart, which forms a series of waves and complexes. Real time identification of these waves and complexes improve the estimation of the future heart motion and improve the performance of the ARMC algorithm. Finally, the experimental results of the algorithm implemented on a 3-DOF robotic test-bed system are reported