Remote sensing helps many applications like precision irrigation, habitat mapping, and traffic monitoring. However, due to shortcomings of current remote sensing platforms - like high cost, low spatial, and temporal resolution - many applications do not have access to useful remote sensing data. A team at the Center for Self-Organizing and Intelligent Systems (CSOIS) together with the Utah Water Research Laboratory (UWRL) at Utah State University has been developing a new remote sensing platform to deal with these shortcomings in order to give more applications access to remote sensing data. This platform (AggieAir) is low cost, fully autonomous, easy to use, independent of a runway, has a fast turnover time, and a high spatial resolution. A program called the Geospatial Real-Time Aerial Image Display (gRAID) has also been developed to process the images taken from AggieAir. gRAID is able to correct the camera lens distortion, georeference, and display the images on a 3D globe, and export them in a conventional Geographic Information System (GIS) format for further processing. AggieAir and gRAID prove to be innovative and useful tools for remote sensing.

This thesis presents work done towards a Personal Remote Sensing (PRS) system: small Unmanned Aerial Vehicles (UAVs) with electronic, control, and sensing subsystems. Based on papers presented to conferences (AutoTestCon2008 and MESA2009), as well as other work on PRS, multiple levels of engineering are detailed: complex multi-UAV data flow; attitude estimation filters; real-time microprocessor functionality; and small, mobile power systems. Wherever possible, Open-Source tools and designs have been used, modified, or studied, providing excellent cost to performance ratios in most cases. First, the overall PRS UAV architecture, AggieAir, is presented with a motivating examples (GhostEye and EagleEye camera payloads). Then, AggieNav, an inertial navigation system for small UAVs, is detailed, along with information about a Kalman filter for estimation of UAV navigation, position, and attitude. Finally the Spatial Environment Autonomous Logger (SEAL), a general-purpose wireless datalogger for small UAV applications, is presented, with application examples with and without small UAVs. This work represents designs based on two years of organic small UAV system growth, and provides integrated solutions to many problems of small UAV communication, sensing, and control.

<p>This thesis investigates the crucial problem of collision avoidance for autonomous vehicles.  An anti-collision system for an unmanned aerial vehicle (UAV) is studied in particular. The purpose of this system is to make sure that the own vehicle avoids collision with other aircraft in mid-air. The sensor used to track any possible threat is for a UAV limited basically to a digital video camera. This sensor can only measure the direction to an intruding vehicle, not the range, and is therefore denoted an angle-only sensor. To estimate the position and velocity of the intruder a tracking system, based on an extended Kalman filter, is used. State estimates supplied by this system are very uncertain due to the difficulties of angle-only tracking. Probabilistic methods are therefore required for risk calculation. The risk assessment module is one of the essential parts of the collision avoidance system and has the purpose of continuously evaluating the risk for collision. To do this in a probabilistic way, it is necessary to assume a probability distribution for the tracking system output. A common approach is to assume normality, more out of habit than on actual grounds. This thesis investigates the normality assumption, and it is found that the tracking output rapidly converge towards a good normal distribution approximation. The thesis furthermore investigates the actual risk assessment module to find out how the collision risk should be determined. The traditional way to do this is to focus on a critical time point (time of closest point of approach, time of maximum collision risk etc.). A recently proposed alternative is to evaluate the risk over a horizon of time. The difference between these two concepts is evaluated. An approximate computational method for integrated risk, suitable for real-time implementations, is also validated. It is shown that the risk seen over a horizon of time is much more robust to estimation accuracy than the risk from a critical time point. The integrated risk also gives a more intuitively correct result, which makes it possible to implement the risk assessment module with a direct connection to specified aviation safety rules.</p>

<p>The use of Unmanned Aerial Vehicles (UAVs) has rapidly increased over the last years. This has been possible mainly due to the increased computing power of microcontrollers and computers. An UAV can be used in both civilian and military areas, for example surveillance and intelligence. The UAV concerned in this master's thesis is a prototype and is currently being developed at DST Control AB in Linköping.</p><p>With the use of UAVs, the need for a positioning and navigation system arises. Inertial sensors can often give a good position estimation, however, they need continuous calibration due to error build-up and drift in gyros. An external reference is needed to correct for this drift and other errors. The positioning system investigated in this master's thesis is supposed to work in an area defined by an inverted cone with the height of 25m and a diameter of 10m.</p><p>A comparison of different techniques suitable for position sensing has been performed. These techniques include the following: a radio method based on the Instrument Landing System (ILS), an optical method using a Position Sensing Detector (PSD), an optical method using the Indoor GPS system, a distance measurement method with ultrasound and also a discussion of the Global Positioning System (GPS).</p><p>An evaluation system has been built using the PSD sensor and tests have been performed to evaluate its possibilities for positioning. Accuracy in the order of a few millimetres has been achieved in position estimation with the evaluation system.</p>

Aeroelastic aspects of unmanned aerial vehicles (UAVs) is analyzed by treatment of a typical section containing geometrical nonlinearities. Equations of motion are derived and numerical integration of these equations subject to quasi-steady aerodynamic forcing is performed. Model properties are tailored to a high-altitude long-endurance unmanned aircraft. Harmonic balance approximation is employed based on the steady-state oscillatory response of the aerodynamic forcing. Comparisons are made between time integration results and harmonic balance approximation. Close agreement between forcing and displacement oscillatory frequencies is found. Amplitude agreement is off by a considerable margin. Additionally, stochastic forcing effects are examined. Turbulent flow velocities generated from the von Karman spectrum are applied to the same nonlinear structural model. Similar qualitative behavior is found between quasi-steady and stochastic forcing models illustrating the importance of considering the non-steady nature of atmospheric turbulence when operating near critical flutter velocity.

This thesis investigates the crucial problem of collision avoidance for autonomous vehicles.  An anti-collision system for an unmanned aerial vehicle (UAV) is studied in particular. The purpose of this system is to make sure that the own vehicle avoids collision with other aircraft in mid-air. The sensor used to track any possible threat is for a UAV limited basically to a digital video camera. This sensor can only measure the direction to an intruding vehicle, not the range, and is therefore denoted an angle-only sensor. To estimate the position and velocity of the intruder a tracking system, based on an extended Kalman filter, is used. State estimates supplied by this system are very uncertain due to the difficulties of angle-only tracking. Probabilistic methods are therefore required for risk calculation. The risk assessment module is one of the essential parts of the collision avoidance system and has the purpose of continuously evaluating the risk for collision. To do this in a probabilistic way, it is necessary to assume a probability distribution for the tracking system output. A common approach is to assume normality, more out of habit than on actual grounds. This thesis investigates the normality assumption, and it is found that the tracking output rapidly converge towards a good normal distribution approximation. The thesis furthermore investigates the actual risk assessment module to find out how the collision risk should be determined. The traditional way to do this is to focus on a critical time point (time of closest point of approach, time of maximum collision risk etc.). A recently proposed alternative is to evaluate the risk over a horizon of time. The difference between these two concepts is evaluated. An approximate computational method for integrated risk, suitable for real-time implementations, is also validated. It is shown that the risk seen over a horizon of time is much more robust to estimation accuracy than the risk from a critical time point. The integrated risk also gives a more intuitively correct result, which makes it possible to implement the risk assessment module with a direct connection to specified aviation safety rules.

The use of Unmanned Aerial Vehicles (UAVs) has rapidly increased over the last years. This has been possible mainly due to the increased computing power of microcontrollers and computers. An UAV can be used in both civilian and military areas, for example surveillance and intelligence. The UAV concerned in this master's thesis is a prototype and is currently being developed at DST Control AB in Linköping. With the use of UAVs, the need for a positioning and navigation system arises. Inertial sensors can often give a good position estimation, however, they need continuous calibration due to error build-up and drift in gyros. An external reference is needed to correct for this drift and other errors. The positioning system investigated in this master's thesis is supposed to work in an area defined by an inverted cone with the height of 25m and a diameter of 10m. A comparison of different techniques suitable for position sensing has been performed. These techniques include the following: a radio method based on the Instrument Landing System (ILS), an optical method using a Position Sensing Detector (PSD), an optical method using the Indoor GPS system, a distance measurement method with ultrasound and also a discussion of the Global Positioning System (GPS). An evaluation system has been built using the PSD sensor and tests have been performed to evaluate its possibilities for positioning. Accuracy in the order of a few millimetres has been achieved in position estimation with the evaluation system.

The development and testing for a UAV testbed to be used in academic research and undergraduate education is proposed in this thesis. Analysis on commercial off-the-shelf UAV systems and autopilots lead to the development of a custom, open-architecture and modular UAV testbed. The main focus is to support research in UAV control field and education of the undergraduate students. The integration and use of commercial-off-the-shelf avionics and air vehicle are described in detail. System performance is examined both in flight and on the ground. Results of the system tests show that the developed system is a functional UAV testbed to be used in research of different flight control algorithms.

The Global Positioning System (GPS) is a cornerstone in Unmanned Aerial Vehicle (UAV) navigation and is by far the most common way to obtain the position of a UAV. However, since there are many scenarios in which GPS measurements might not be available, the possibility of estimating the UAV position without using the GPS would greatly improve the overall robustness of the navigation. This thesis studies the possibility of instead using Simultaneous Localisation and Mapping (SLAM) in order to estimate the position of a UAV using an Inertial Measurement Unit (IMU) and the direction towards ground based radio transmitters without prior knowledge of their position. Simulations using appropriately generated data provides a feasibility analysis which shows promising results for position errors for outdoor trajectories over large areas, however with some issues regarding overall offset. The method seems to have potential but further studies are required using the measurements from a live flight, in order to determine the true performance.

A common problem in existing Search And Rescue (SAR) systems is that they must be activated by the missing person in order to work. This requires an awareness of the the risk of becoming distressed, which in many cases is not feasible. Furthermore, most of the localization systems require specialized hardware.In this thesis, the victim is assumed to wear a cellphone that could be located using readily available consumer electronics. A method of estimating the position of a transmitter, given radio signal measurements at different locations, is developed and verified with real and simulated data. A proof-of-concept system is built in which several users can jointly collect received signal strength data at different locations using mobile phones. The system analyzes the data in real-time and guides the users in the search by estimating the origin of the signal.An outdoor field test is conducted in which the searchers using the system are able to locate the hidden target phone without prior knowledge regarding the position. We are able to localize the victim with an accuracy of 10-20 meters in a timely manner using android smartphones. This shows the potential of a similar system in \abbrSAR scenarios. However, more work is needed to make the system viable in real scenarios and to remove some of the delimitations of the current implementation.