Concept and Feasibility Evaluation of Distributed Sensor-Based Measurement Systems Using Formation Flying Multicopters

Unmanned aerial vehicles (UAVs) have been used for increasing research applications in atmospheric measurements. However, most current solutions for these applications are based on a single UAV with limited payload capacity. In order to address the limitations of the single UAV-based approach, this paper proposes a new concept of measurements using tandem flying multicopters as a distributed sensor platform. Key challenges of the proposed concept are identified including the relative position estimation and control in wind-perturbed outdoor environment and the precise alignment of payloads. In the proposed concept, sliding mode control is chosen as the relative position controller and a gimbal stabilization system is introduced to achieve fine payload alignment. The characterization of the position estimation sensors (including global navigation satellite system and real-time kinematics) and flight controller is carried out using different UAVs (a DJI Matrice M600 Pro Hexacopter and Tarot X4 frame based Quadcopter) under different wind levels. Based on the experimental data, the performance of the sliding mode controller and the performance of the gimbal stabilization system are evaluated in a hardware-in-the-loop simulation environment (called ELISSA). Preliminary achievable control accuracies of the relative position and attitude of subsystems in the proposed concept are estimated based on experimental result

New Setup of the UAS ALADINA for Measuring Boundary Layer Properties, Atmospheric Particles and Solar Radiation

The unmanned research aircraft ALADINA (Application of Light-weight Aircraft for Detecting in situ Aerosols) has been established as an important tool for boundary layer research. For simplified integration of additional sensor payload, a flexible and reliable data acquisition system was developed at the Institute of Flight Guidance, Technische Universität (TU) Braunschweig. The instrumentation consists of sensors for temperature, humidity, three-dimensional wind vector, position, black carbon, irradiance and atmospheric particles in the diameter range of ultra-fine particles up to the accumulation mode. The modular concept allows for straightforward integration and exchange of sensors. So far, more than 200 measurement flights have been performed with the robustly-engineered system ALADINA at different locations. The obtained datasets are unique in the field of atmospheric boundary layer research. In this study, a new data processing method for deriving parameters with fast resolution and to provide reliable accuracies is presented. Based on tests in the field and in the laboratory, the limitations and verifiability of integrated sensors are discussed

Training in the practice of noninvasive brain stimulation: Recommendations from an IFCN committee

© 2020 As the field of noninvasive brain stimulation (NIBS) expands, there is a growing need for comprehensive guidelines on training practitioners in the safe and effective administration of NIBS techniques in their various research and clinical applications. This article provides recommendations on the structure and content of this training. Three different types of practitioners are considered (Technicians, Clinicians, and Scientists), to attempt to cover the range of education and responsibilities of practitioners in NIBS from the laboratory to the clinic. Basic or core competencies and more advanced knowledge and skills are discussed, and recommendations offered regarding didactic and practical curricular components. We encourage individual licensing and governing bodies to implement these guidelines

Aviation Applications: Hybrid Navigation Techniques and Safety-of-Life Requirements, Part 1

This paper describes some key results of the UniTaS IV project, a publicly funded effort to investigate special problems in the application of satellite navigation for aviation. Among the subjects covered: adaptive beamforming antennas, a GNSS landing system that incorporates inertial sensors with a ground-based augmentation system (GBAS), multiconstellation RAIM (receiver autonomous integrity monitoring), and jamming, spoofing, and authentication of signals

A Flight State Estimator that Couples Stereo-Vision, INS, and GNSS Pseudo-Ranges to Navigate with Three or Less Satellites

This paper presents a flight state estimator which couples stereo vision, inertial (INS), and global navigation satellite system (GNSS) data. The navigation filter comes with different operation modes that allow loosely coupled GNSS/INS positioning and, for difficult conditions, improvements using visual odometry and a tighter coupling with GNSS pseudo-range (PSR) data. While camera systems are typically used as an additional relative movement sensor to enable positioning without GNSS for a certain amount of time, the PSR data filtering allows to use satellite navigation also when less than four satellites are available. This makes the filter even more robust against temporary dropouts of the full GNSS solution. The application is the navigation of unmanned aircraft in disaster scenarios which includes flights close to ground in urban or mountainous areas. The filter performance is evaluated with sensor data from unmanned helicopter flight tests where different conditions of the GNSS signal reception are simulated. It is shown that the use of PSR data improves the positioning significantly compared to the dropout when the signals of less than four satellites are available