A compilation of UAV applications for precision agriculture

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Aided Navigation Gps With High Rate Sensors

The paradigm-changing opportunities of biologging sensors for ecological research, especially movement ecology, are vast, but the crucial questions of how best to match the most appropriate sensors and sensor combinations to specific biological questions and how to analyse complex biologging data, are mostly ignored. Here, we fill this gap by reviewing how to optimize the use of biologging techniques to answer questions in movement ecology and synthesize this into an Integrated Biologging Framework (IBF). We highlight that multisensor approaches are a new frontier in biologging, while identifying current limitations and avenues for future development in sensor technology. We focus on the importance of efficient data exploration, and more advanced multidimensional visualization methods, combined with appropriate archiving and sharing approaches, to tackle the big data issues presented by biologging. We also discuss the challenges and opportunities in matching the peculiarities of specific sensor data to the statistical models used, highlighting at the same time the large advances which will be required in the latter to properly analyse biologging data. Taking advantage of the biologging revolution will require a large improvement in the theoretical and mathematical foundations of movement ecology, to include the rich set of high-frequency multivariate data, which greatly expand the fundamentally limited and coarse data that could be collected using location-only technology such as GPS. Equally important will be the establishment of multidisciplinary collaborations to catalyse the opportunities offered by current and future biologging technology. If this is achieved, clear potential exists for developing a vastly improved mechanistic understanding of animal movements and their roles in ecological processes and for building realistic predictive models.

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Optimizing the use of biologgers for movement ecology research

A highly sensitive flexible magnetic sensor based on the anisotropic magnetoresistance effect is fabricated. A limit of detection of 150 nT is observed and excellent deformation stability is achieved after wrapping of the flexible sensor, with bending radii down to 5 mm. The flexible AMR sensor is used to read a magnetic pattern with a thickness of 10 μm that is formed by ferrite magnetic inks.

Highly Sensitive Flexible Magnetic Sensor Based on Anisotropic Magnetoresistance Effect

This paper proposes a calibration procedure in order to minimize the process time and cost. It relies on the suggestion of optimal positions, in which the calibration procedure takes place, and on position number optimization. Furthermore, this paper describes and compares three useful calibration algorithms applicable on triaxial accelerometer to determine its mathematical error model without a need to use an expensive and precise calibration means, which is commonly required. The sensor error model (SEM) of triaxial accelerometer consists of three scale-factor errors, three nonorthogonality angles, and three offsets. For purposes of calibration, two algorithms were tested-the Levenberg-Marquardt and the Thin-Shell algorithm. Both were then related to algorithm based on Matlab fminunc function to analyze their efficiency and results. The proposed calibration procedure and applied algorithms were experimentally verified on accelerometers available on market. We performed various analyses of proposed procedure and proved its capability to estimate the parameters of SEM without a need of precise calibration means, with minimum number of iteration, both saving time, workload, and costs.

Analyses of Triaxial Accelerometer Calibration Algorithms

Accelerometers (ACCs) and gyroscopes (gyros) are commonly known as inertial sensors and their orthogonal triads generally form an inertial measurement unit (IMU) used as a core means of a navigation system. Before the navigation system is to be used, it is necessary to perform its calibration. A typical process of the IMU calibration usually estimates scale-factors, orthogonality or misalignment errors, and offsets of both triads. These parameters compose the so-called sensor error model (SEM). The process of obtaining accurate information that describes the motion performed within the calibration generally requires a costly and specialized means [1], [2]. Therefore, much effort has been put into cost-effective calibration using an optical motion tracking system [3]–[6], or transferring the calibration into a state estimation problem [7]. In the ACC case, most of the current calibration methods utilize the fact that ACCs are affected by gravity when they are under static conditions. Therefore, we proceed with calibration performed under static conditions, which utilizes the knowledge of the gravity magnitude and ACC output measurements collected at predetermined orientations and performs ACC SEM estimation using nonlinear optimization [6], [8]–[10]. In the gyro case, calibration based on the Earth's rate might be inapplicable, for instance due to the fact that Earth's rate is under or around the resolution of the gyro, and thus other means to apply and measure angular rates need to be used. This situation commonly arises in the case of low-cost MEMS (Micro-Electro-Mechanical System) based gyros. Thus, expensive mechanical platforms are often inevitable [11]–[14].

Calibration of low-cost triaxial inertial sensors

With advanced control, estimation and simulation requirements in unmanned aerial systems comes the need for sophisticated aerodynamic models. This paper reviews two common means for establishing such models; numerical design tools and wind tunnel testing, by presenting strengths and potential problems, in a ”lessons learned”-manner. As a case study throughout the paper, a six degrees-of-freedom aerodynamic model of the Skywalker X8 fixed-wing unmanned aerial vehicle is presented.

/pdf/aerodynamic-modeling-of-the-skywalker-x8-fixed-wing-unmanned-3c90rzqoes.pdf

Aerodynamic modeling of the Skywalker X8 Fixed-Wing Unmanned Aerial Vehicle

The paper describes a performance analysis of two low-cost AHRS (Attitude and Heading Reference Systems), calibration procedures, and the verification of INS (Inertial Navigation System) mechanization algorithm using dedicated automatic measurement system based on a real-time flight simulation. The measurement system included the flight simulation software FlightGear (FG) that offered a wide range of aircraft dynamics and track simulation possibilities. The FG output data were converted into the form suitable for a servo-controlled Rotational-Tilt Platform (RoTiP) which provided corresponding motion for two AHRS units mounted on it and reference information from optical sensors. The output data of the AHRS units were collected, processed and evaluated to verify the units accuracy and reliability. The methodology and results based on the performance analyses are presented.

Flight attitude track reconstruction using two AHRS units under laboratory conditions

MEMS (micro-electro-mechanical system)-based inertial sensors, i.e., accelerometers and angular rate sensors, are commonly used as a cost-effective solution for the purposes of navigation in a broad spectrum of terrestrial and aerospace applications. These tri-axial inertial sensors form an inertial measurement unit (IMU), which is a core unit of navigation systems. Even if MEMS sensors have an advantage in their size, cost, weight and power consumption, they suffer from bias instability, noisy output and insufficient resolution. Furthermore, the sensor's behavior can be significantly affected by strong vibration when it operates in harsh environments. All of these constitute conditions require treatment through data processing. As long as the navigation solution is primarily based on using only inertial data, this paper proposes a novel concept in adaptive data pre-processing by using a variable bandwidth filtering. This approach utilizes sinusoidal estimation to continuously adapt the filtering bandwidth of the accelerometer's data in order to reduce the effects of vibration and sensor noise before attitude estimation is processed. Low frequency vibration generally limits the conditions under which the accelerometers can be used to aid the attitude estimation process, which is primarily based on angular rate data and, thus, decreases its accuracy. In contrast, the proposed pre-processing technique enables using accelerometers as an aiding source by effective data smoothing, even when they are affected by low frequency vibration. Verification of the proposed concept is performed on simulation and real-flight data obtained on an ultra-light aircraft. The results of both types of experiments confirm the suitability of the concept for inertial data pre-processing.

Jan Rohac

Papers

Analyses of Triaxial Accelerometer Calibration Algorithms

Calibration of low-cost triaxial inertial sensors

Aerodynamic modeling of the Skywalker X8 Fixed-Wing Unmanned Aerial Vehicle

Flight attitude track reconstruction using two AHRS units under laboratory conditions

Adaptive Data Filtering of Inertial Sensors with Variable Bandwidth