MaDoPO: Magnetic Detection of Positions and Orientations of Segmented Deep-Brain Stimulation Electrodes: A Radiation-Free Method Based on Magnetoencephalography

Background: Current approaches to detect the positions and orientations of directional deep-brain stimulation (DBS) electrodes rely on radiative imaging data. In this study, we aim to present an improved version of a radiation-free method for magnetic detection of the position and the orientation (MaDoPO) of directional electrodes based on a series of magnetoencephalography (MEG) measurements and a possible future solution for optimized results using emerging on-scalp MEG systems. Methods: A directional DBS system was positioned into a realistic head–torso phantom and placed in the MEG scanner. A total of 24 measurements of 180 s each were performed with different predefined electrode configurations. Finite element modeling and model fitting were used to determine the position and orientation of the electrode in the phantom. Related measurements were fitted simultaneously, constraining solutions to the a priori known geometry of the electrode. Results were compared with the results of the high-quality CT imaging of the phantom. Results: The accuracy in electrode localization and orientation detection depended on the number of combined measurements. The localization error was minimized to 2.02 mm by considering six measurements with different non-directional bipolar electrode configurations. Another six measurements with directional bipolar stimulations minimized the orientation error to 4°. These values are mainly limited due to the spatial resolution of the MEG. Moreover, accuracies were investigated as a function of measurement time, number of sensors, and measurement direction of the sensors in order to define an optimized MEG device for this application. Conclusion: Although MEG introduces inaccuracies in the detection of the position and orientation of the electrode, these can be accepted when evaluating the benefits of a radiation-free method. Inaccuracies can be further reduced by the use of on-scalp MEG sensor arrays, which may find their way into clinics in the foreseeable future.

Mobile, Modular and Adaptive Assembly Jigs for Large-Scale Products

The assembly of products is often supported by jigs. Especially for large dimensional products, jigs and fixtures are used to align the components and ensure the stability of the assembly until all parts are firmly mounted. This paper describes the development of mobile, modular and adaptive assembly jigs, which are designed to support ergonomic working in the production of high-lift systems for civil aircrafts. The jig supports the workers to adapt the position and orientation of the product to the current assembly operation. The fundamentals of the development are explained and the features of a concept, called assembly wheel, are presented. The assembly wheel consists of two or more robot arms on a circular seventh axis. The robot arms hold and position the components to be assembled so that all joining spots are freely accessible to the worker. The ergonomic benefits of the concept were examined in a study using a 3D model of the jig. A demonstrator on a scale of 1:2 was set up, with which real experiments with an adaptive jig can be conducted for evaluation.

ALGORITHM FOR PRECISE LANDING OF MULTIROTOR UNMANNED AIRCRAFT SYSTEMS AT AN AUTONOMOUS CHARGING STATION

In this article, we propose an algorithm for accurately landing multirotor (quadcopters, hexacopters, etc.) unmanned aerial vehicles (UAVs) at an autonomous charging station. This article also presents methods for locating the charging station and landing the UAV at night. Section 1 describes the general sequential landing procedures. Section 2 describes methods for detecting the ArUco marker and evaluating its position and orientation using the OpenCV computer vision library and shows the recognition result. In section 3, the precise landing algorithm is analyzed in detail, and a block diagram of the algorithm is given. Section 4 discusses the integration of the night vision camera into the landing algorithm.

Robotic Forklift for Stacking Multiple Pallets with RGB-D Cameras

We propose a robotic forklift system for stacking multiple mesh pallets. The stacking of mesh pallets is an essential task for the shipping and storage of loads. However, stacking, the placement of pallet feet on pallet edges, is a complex problem owing to the small sizes of the feet and edges, leading to a complexity in the detection and the need for high accuracy in adjusting the pallets. To detect the pallets accurately, we utilize multiple RGB-D (RGB Depth) cameras that produce dense depth data under the limitations of the sensor position. However, the depth data contain noise. Hence, we implement a region growing-based algorithm to extract the pallet feet and edges without removing them. In addition, we design the control law based on path following control for the forklift to adjust the position and orientation of two pallets. To evaluate the performance of the proposed system, we conducted an experiment assuming a real task. The experimental results demonstrated that the proposed system can achieve a stacking operation with a real forklift and mesh pallets.

Innovative Differential Magnetic Localization Method for Capsule Endoscopy to Prevent Interference Caused by the Geomagnetic Field

Wireless capsule endoscopy is an established medical application for the examination of the gastrointestinal tract. However, the robust and precise localization of these capsules is still in need of further scientific investigation. This paper presents an innovative differential magnetic localization method for capsule endoscopy to prevent interference caused by the geomagnetic field. The effect of changing the orientation of the capsule on the localization process was also examined. Simulations using COMSOL Multiphysics with the superimposed geomagnetic field were performed. The Levenberg–Marquardt algorithm was applied in MATLAB to estimate the position and orientation of the capsule. Comparing the proposed differential method with the absolute magnetic localization method under ideal conditions, the mean position and orientation errors were reduced by three orders in magnitude to less than 0.1 mm and 0.1∘ respectively. Even if sensor non-idealities are considered, the simulation-based results reveal that our proposed method is competitive with state-of-the-art geomagnetic compensation methods for static magnetic localization of capsule endoscopes. The achieved localization accuracy by applying the differential method is not dependent on the rotation of the localization system relative to the geomagnetic flux density under the made assumptions and the impact of the magnet orientation is neglectable. It is concluded that the proposed method is capable of preventing all interference whose components are approximately equal at all sensors with identical orientation.

3D Contour Shaping of Buried Objects in Soil

The basic question of this paper was, whether a detected anomaly found in the ground during an explosives disposal process is actually a non-detonated bomb or non-dangerous metallic scrap. Based on a borehole radar, an approach is to be presented in which first a 2-dimensional contour of the object is created with the aid of a spatial runtime evaluation. By repeating this step at different depths with subsequent graphic overlay, a 3D shape of the buried object is created. The method is first tested using a simulation model with inhomogeneous soil. In the second step the method will be applied and evaluated using a field measurement of a real object. The results shows that both 2D and 3D evaluations reflect the position and orientation of the object. Furthermore, the shape and the dimensions can be estimated, with the restriction that the 3D contour has distortions along the vertical axis. The aim of this work is to show an application of borehole radar, with which the identification of buried objects should be facilitated.