International Telemetering Conference Proceedings / October 20-23, 2003 / Riviera Hotel and Convention Center, Las Vegas, Nevada / A Flight Safety System (RAFS) for multiple, reliable Unmanned Air Vehicles (UAV’s) capable of flying Over-the-Horizon (OTH) and outside test range airspace. In addition to the flight safety application, the described full-duplex data link is suitable as a backup command and control link for UAV’s, and for sensor control & data exfiltration. The IRIDIUM satellite system was selected to provide the communications link and because of its global coverage and requisite data throughputs. A Risk Reduction activity ensued to quantify IRIDIUM performance. Hardware and software was developed to demonstrate the feasibility of using IRIDIUM in a flight safety scenario.

International Telemetering Conference Proceedings / October 21, 2002 / Town & Country Hotel and Conference Center, San Diego, California / This paper presents a Flight Safety System (FSS) for multiple, reliable Unmanned Air Vehicles (UAV’s) capable of flying Over-the-Horizon (OTH) and outside test range airspace. Expanded uses beyond flight safety, such as UAV Air Traffic Control, are considered also. This system satisfies the operational requirement for a Hazard Control Communication Channel as well as providing a reverse communications channel to provide Safety Critical Information to the Range Safety Officer (RSO). Upon examining 60 communications candidates, IRIDIUM accessed through a Data Distribution Network (DDN), with ARINC being a potential service provider, is recommended.

Unmanned Air Vehicles (UAVs) are becoming more widely used in both military and civilian applications. Some of the largest UAVs have power systems equivalent to that of a military strike jet making power management an important aspect of their design. As they have developed, the amount of power needed for loads has increased. This has placed increase strain on the on-board generators and a need for higher reliability. In normal operation these generators are sized to be able to power all on-board systems with out overheating. Under abnormal operating conditions these generators may start to overheat, causing the loss of the generator's power output. The research presented here aims to answer two main questions: 1) Is it possible to predict when an overheat fault will occur based on the expected power usage defined by mission profiles? 2) Can an overheat fault be prevented while still allowing power to be distributed to necessary loads to allow mission completion? This is achieved by a load management algorithm, which adjusts the load profile for a mission, by either displacing the load to spare generators, or resting the generator to cool it down. The result is that for non-catastrophic faults the faulty generator does not need to be fully shut down and missions can continue rather than having to be aborted. This thesis presents the development of the load management system including the algorithm, prediction method and the models used for prediction. Ultimately, the algorithms developed are tested on a generator test rig. The main contribution of this work is the design of a prognostic load management algorithm. Secondary contributions are the use of a lumped parameter thermal model within a condition monitoring application, and the creation of a system identification model to describe the thermal dynamics of a generator.

In this thesis, automatic routing technologies for unmanned air vehicles are investigated. A route planner that minimizes the fuel consumption and maximizes the survivability is developed. While planning the route, using more than one objective entails the auto-routing problem to multi-objective optimization considerations. In this work, these considerations are handled with search algorithms. In order to assess the route options, a fuel consumption model and a survivability model are utilized for the route planner. As the assessment models are established, required computational time is taken into account without deteriorating the fidelity.

Unmanned Air Vehicles (UAVs) enable the observation of hazardous areas without endangering a pilot. Observational capabilities are provided by on-board video cameras and images are relayed to remote operators for analysis. However, vibration and wind cause video camera mounts to move and can introduce unintended motion that makes video analysis more difficult. Video stabilization is a process that attempts to remove unwanted movement from a video input to provide a clearer picture. This thesis presents an onboard video stabilization solution that removes high-frequency jitter, displays output at 20 frames per second (FPS), and runs on a Blackfin embedded processor. Any video stabilization algorithm will have to contend with the limited space, weight, and power available for embedded systems hardware on a UAV. This thesis demonstrates how architecture-specific optimizations improve algorithm performance on embedded systems and allow an algorithm that was designed with more powerful computing systems in mind to perform on a system that is limited in both size and resources. These optimizations reduce the total clock cycles per frame by 157 million to 30 million, which yields a frame rate increase from 3.2 to 20 FPS.

No description available.

Aerial recovery of autonomous micro air vehicles (MAVs) presents many unique challenges due to the difference in size and speed of the recovery vehicle and MAV. This thesis presents algorithms to enable an autonomous MAV to estimate the orbit of a recovery vehicle and track the orbit until the final docking phase. Methods for estimating ellipses that are rotated out of the x − y plane are developed and demonstrated through simulation. These algorithms are shown to be robust to noise and stable numerically. Parameter update methods that are computationally inexpensive, such as recursive least squares and Kalman filtering, are discussed and simulated. A discussion is given of orbit tracking algorithms for circular orbits, and these methods are expanded to include elliptical orbits. These algorithms enable the MAV to track the recovery vehicle's orbit, based on a vector field approach. The tracking algorithms are divided into lateral and longitudinal controllers that allow for tracking of inclined orbits. Finally, the hardware and software setup for live flight tests is discussed. Flight test results are given that validate the functionality of the orbit estimation and orbit tracking algorithms.

This work presents two novel methods of estimating the state of a dynamic system in a Kalman Filtering framework. The first is an application specific method for use with systems performing Visual Odometry in a mostly planar scene. Because a Visual Odometry method inherently provides relative information about the pose of a platform, we use this system as part of the time update in a Kalman Filtering framework, and develop a novel way to propagate the uncertainty of the pose through this time update method. Our initial results show that this method is able to reduce localization error significantly with respect to pure INS time update, limiting drift in our test system to around 30 meters for tens of seconds. The second key contribution of this work is the Manifold EKF, a generalized version of the Extended Kalman Filter which is explicitly designed to estimate manifold-valued states. This filter works for a large number of commonly useful manifolds, and may have applications to other manifolds as well. In our tests, the Manifold EKF demonstrated significant advantages in terms of consistency when compared to other filtering methods. We feel that these promising initial results merit further study of the Manifold EKF, related filters, and their properties.

Modern radar technology relies heavily on digital signal processing. As radar technology pushes the boundaries of miniaturization, computational systems must be developed to support the processing demand. One particular application for small radar technology is in modern drone systems. Many drone applications are currently inhibited by safety concerns of autonomous vehicles navigating shared airspace. Research in radar based Detect and Avoid (DAA) attempts to address these concerns by using radar to detect nearby aircraft and choosing an alternative flight path. Implementation of radar on small Unmanned Air Vehicles (UAV), however, requires a lightweight and power efficient design. Likewise, the radar processing system must also be small and efficient.This thesis presents the design of the processing system for a small Frequency Modulated Continuous Wave (FMCW) phased array radar. The radar and processing is designed to be light-weight and low-power in order to fly onboard a UAV less than 25 kg in weight. The radar algorithms for this design include a parallelized Fast Fourier Transform (FFT), cross correlation, and beamforming. Target detection algorithms are also implemented. All of the computation is performed in real-time on a Xilinx Zynq 7010 System on Chip (SoC) processor utilizing both FPGA and CPU resources.The radar system (excluding antennas) has dimensions of 2.25 x 4 x 1.5 in3, weighs 120 g, and consumes 8 W of power of which the processing system occupies 2.6 W. The processing system performs over 652 million arithmetic operations per second and is capable of performing the full processing in real-time. The radar has also been tested in several scenarios both airborne on small UAVs as well as on the ground. Small UAVs have been detected to ranges of 350 m and larger aircraft up to 800 m. This thesis will describe the radar design architecture, the custom designed radar hardware, the FPGA based processing implementations, and conclude with an evaluation of the system's effectiveness and performance.

Coalitions are collections of agents that join together to solve a common problem that either cannot be solved individually or can be solved more efficiently as a group. Each individual agent has capabilities that can benefit the group when working together as a coalition. Typically, individual capabilities are joined together in an additive way when forming a coalition. This work will introduce a new operator that is used when combining capabilities, and suggest that the behavior of the operator is contextual, depending on the nature of the capability itself. This work considers six different capabilities of Unmanned Air Vehicles (UAV) and determines the nature of the new operator in the context of each capability as coalitions (squadrons) of UAVs are formed. Coalitions are formed using three different search algorithms, both with and without heuristics: Depth-First, Depth-First Iterative Deepening, and Genetic Algorithm (GA). The effectiveness of each algorithm is evaluated. Multi agent-based UAV simulation software was developed and used to test the ideas presented. In addition to coalition formation, the software aims to address additional multi-agent issues such as agent identity, mutability, and communication as applied to UAV systems, in a realistic simulated environment. Social potential fields provide a means of modeling a clustering attractive force at the same time as a collision-avoiding repulsive force, and are used by the simulation to maintain aircraft position relative to other UAVs.