Assessment of air quality has been traditionally conducted by ground based monitoring, and more recently by manned aircrafts and satellites. However, performing fast, comprehensive data collection near pollution sources is not always feasible due to the complexity of sites, moving sources or physical barriers. Small Unmanned Aerial Vehicles (UAVs) equipped with different sensors have been introduced for in-situ air quality monitoring, as they can offer new approaches and research opportunities in air pollution and emission monitoring, as well as for studying atmospheric trends, such as climate change, while ensuring urban and industrial air safety. The aims of this review were to: (1) compile information on the use of UAVs for air quality studies; and (2) assess their benefits and range of applications. An extensive literature review was conducted using three bibliographic databases (Scopus, Web of Knowledge, Google Scholar) and a total of 60 papers was found. This relatively small number of papers implies that the field is still in its early stages of development. We concluded that, while the potential of UAVs for air quality research has been established, several challenges still need to be addressed, including: the flight endurance, payload capacity, sensor dimensions/accuracy, and sensitivity. However, the challenges are not simply technological, in fact, policy and regulations, which differ between countries, represent the greatest challenge to facilitating the wider use of UAVs in atmospheric research.

An Overview of Small Unmanned Aerial Vehicles for Air Quality Measurements: Present Applications and Future Prospectives

Additive manufacturing in unmanned aerial vehicles (UAVs): Challenges and potential

The meteorological mini unmanned aerial vehicle (M2AV) was used for measuring the meteorological wind. The wind is the vector difference between the aircraft speed relative to the earth (inertial velocity) and relative to the airflow (true airspeed). The latter was computed from five-hole-probe pressure measurements in combination with calibration–coefficient polynomials obtained during wind tunnel calibration. The aircraft inertial velocity, position, and attitude were calculated using a Kalman filter that combined data from a global positioning system (GPS) and an inertial navigation system (INS). The temporal (and spatial) resolution of the M2AV wind measurement is remarkably fine. An inertial subrange of locally isotropic turbulence can be measured up to 40 Hz (or 0.55 m at 22 m s−1 airspeed). The first M2AV wind estimation showed some systematic deviations compared to the expected values (like a constant mean wind in every flight direction). Therefore, an in-flight wind calibration technique...

Measuring the Wind Vector Using the Autonomous Mini Aerial Vehicle M2AV

Although potential applications abound, small-scale unmanned aerial vehicles have not yet been widely used for environmental remote sensing. Several challenges remain to be overcome until widesprea...

Small-Scale Unmanned Aerial Vehicles in Environmental Remote Sensing: Challenges and Opportunities

The development of low-cost unmanned aerial vehicles (UAVs) and light weight imaging sensors has resulted in significant interest in their use for remote sensing applications. While significant attention has been paid to the collection, calibration, registration and mosaicking of data collected from small UAVs, the interpretation of these data into semantically meaningful information can still be a laborious task. A standard data collection and classification work-flow requires significant manual effort for segment size tuning, feature selection and rule-based classifier design. In this paper, we propose an alternative learning-based approach using feature learning to minimise the manual effort required. We apply this system to the classification of invasive weed species. Small UAVs are suited to this application, as they can collect data at high spatial resolutions, which is essential for the classification of small or localised weed outbreaks. In this paper, we apply feature learning to generate a bank of image filters that allows for the extraction of features that discriminate between the weeds of interest and background objects. These features are pooled to summarise the image statistics and form the input to a texton-based linear classifier that classifies an image patch as weed or background. We evaluated our approach to weed classification on three weeds of significance in Australia: water hyacinth, tropical soda apple and serrated tussock. Our results showed that collecting images at 5–10 m resulted in the highest classifier accuracy, indicated by F1 scores of up to 94%.

Feature Learning Based Approach for Weed Classification Using High Resolution Aerial Images from a Digital Camera Mounted on a UAV

The limitations of manned airborne meteorological measurements led to a new unmanned system, the Meteorological Mini-UAV (M 2 AV), recently developed by the Institute of Aerospace Systems, Technical University of Braunschweig. The task was to develop, test and verify a meteorological sensor package as payload for an already available carrier aircraft, the UAV ‘Carolo T200’. Thereby the limitations in size and mass had to be respected. The M 2 AV is capable of performing turbulence and wind vector measurements within the atmospheric boundary layer and permits very short measurement cycles as an economic supplement during meteorological campaigns. The article gives details on the technical items. Results from meteorological data sets measured by the M 2 AV are used for data quality assessment. In October 2005 the M 2 AV participated in the meteorological field experiment ‘LAUNCH 2005’ in Linden berg near Berlin. The M 2 AV data were compared with lidar and sodar/RASS measurements. Furthermore, an in situ comparison of temperature, humidity and wind vector data with the helicopter-borne turbulence probe Helipod was analysed and gave information about the M 2 AV data quality. Zusammenfassung Dieser Artikel befasst sich mit dem unbemannten meteorologischen Kleinflugzeug (M 2 AV), das vom Institut fur Luft- und Raumfahrtsysteme der Technischen Universitat Braunschweig entwickelt wurde. Die Grenzen bemannter Flugmessungen fuhrten zur Entwicklung eines neuartigen, unbemannten meteorologischen Flugmesssystems, dem M 2 AV. Die Aufgabe bestand darin, ein Sensorpaket fur ein bestehendes Tragerflugzeug, das unbemannte Kleinstflugzeug ‘Carolo T200’ zu en twickeln, zu testen und zu verifizieren. Dabei mussten insbesondere die Rahmenbedingungen bezuglich der Grose und der Masse beachtet werden. Trotzdem sollte das M 2 AV in der Lage sein, innerhalb der atmospharischen Grenzschicht den Windvektor zu messen und Turbulenz aufzulosen. Das entwickelte System gestattet dicht aufeinanderfolgende Messungen als okonomische Unterstutzung wahrend meteorologischer Kampagnen. Es wird die Technik des M 2 AV im Detail beschrieben und die Ergebnisse aus der wissenschaftlichen Untersuchung von meteorologischen Datensatzen verwendet, um die Datenqualitat und die raumliche Auflosung einzuschatzen.

First application of the meteorological Mini-UAV 'M2AV'

1 INTRODUCTION Micro Aerial Vehicles (MAV) form a comparably new area of aeronautical research. This type of aircraft is defined by takeoff weights typically less than 500 g and very small dimensions, e.g. wingspans under 50 cm. Intense research is conducted worldwide to further reduce MAV mass and size (e.g. Within this paper, this term is expanded to also include so-called miniature unmanned aerial vehicles (Mini-UAV), which comprise typically of aircraft with a wingspan of a few meters and several kg of takeoff mass. Most current MAV projects concentrate on size and mass but neglect autonomous operation which means a pilot on the ground has to control the aircraft manually. The main research activity at the Institute of Aerospace Systems (ILR) of the Technische Universität Braunschweig, Germany, is the development of a fully autonomous MAV, operating without any intervention from ground.

MMAV—a miniature unmanned aerial vehicle (Mini-UAV) for meteorological purposes

This paper describes the current development of intelligent vision capabilities for small sized Unmanned Aerial Vehicles (UAV) as part of the program “CAROLO” at the Technical University of Braunschweig, Germany. The objective of this development is to create image processing algorithms and appropriate hardware that suits the requirements of small sized UAVs. Enabling vision-based flight guidance during landing was chosen as the first task and acts as a technology demonstrator for future vision capabilities. The characteristics of a laid out marker are extracted from the on-board imagery. Relative position and attitude of this marker relative to the body-fixed coordinate system are determined. This concept conveys information about the designated landing point to the aircraft. An experimental installation of the vision system was used for first measurements. This experiment and its results – in particular the attainable accuracies of the presented vision system, are the subject of this publication.

Marco Buschmann

Papers

Measuring the Wind Vector Using the Autonomous Mini Aerial Vehicle M2AV

First application of the meteorological Mini-UAV 'M2AV'

MMAV—a miniature unmanned aerial vehicle (Mini-UAV) for meteorological purposes

Towards Vision-Based Autonomous Landing for Small UAVs - First Experimental Results of the Vision System

Progresses in the Development of the Fully Autonomous MAV "CAROLO"