William H. Semke

Unmanned Aircraft Systems Sense and Avoid Avionics Utilizing ADS -B Transceiver

To integrate Unmanned Aircraft Systems (UAS) into the U.S. National Airspace (NAS), the Federal Aviation Administration (FAA) requires “an equivalent level of safety, comparable to see -and -avoid requirements for manned aircraft.” A pr oposed FAA rulemaking would mandate Automatic Dependent Surveillance – Broadcast ( ADS -B) out equipage by 2020, forcing aircraft flying in the NAS to broadcast their state vector to Air Traffic Control (ATC) and aircraft equipped with ADS -B in capability . With the advent of lightweight, low power, and low -cost ADS -B units, the equipage and the use of ADS -B transceivers on small UAS are no longer limited by payload and power capabilities , and represent promising opportunities for the regulated operation of sm all UAS in the NAS. T he University of North Dakota (UND) has been actively developing enabling sense and avoid (SAA) technologies under a series of projects funded by the Department of Defense , and is now turning to ADS -B as the central component for mid -air collision avoidance . This paper presents the results of modeling collision avoidance algorithms using ADS -B derived information in a software -in -the -loop (SWIL) environment . Upon extensive testing, the system is designed to be integrated seamlessly in to a hardware -in -the -loop (HWIL) environment, and ultimately in a flight test environment aboard a small UAS.

Quantitative evaluation of image mosaicing in multiple scene categories

Image mosaicing has been practiced in several computer vision and scientific research areas. There is a clear indication of the advancement of the state of the art of mosaicing algorithms. However, the methods of quantitative evaluation of mosaicing algorithms are still inadequate. Furthermore, a majority of the previous evaluation methodologies lack a sufficient number of performance metrics, while others suffer from computational complication. Therefore, this paper proposes an evaluation method to assess the performance of mosaicing algorithms. This method involves four metrics: percentage of mismatches, difference of pixel intensities, peak signal-to-noise ratio, and mutual information to measure the quality of the mosaicing outputs. These outputs are obtained using a mosaicing algorithm based on the Scale Invariant Feature Transform, Best Bins First, and Random Sample Consensus, reprojection and stitching algorithms. In order to evaluate mosaicing performance objectively, the proposed method compares mosaicing images with the ground-truth images that depict the same scene view. Evaluation has been performed using 36 test sequences from 3 different categories: images of 2D surfaces, images of outdoor 3D scenes, and airborne images from an Unmanned Aerial Vehicle. Exhaustive testing has shown that the proposed metrics are effective in assessing the quality of mosaicing outputs.

Vision-Based Autopilot Implementation Using a Quadrotor Helicopter

A monocular vision-based pose estimation and stabilization system for a quadrotor helicopter is proposed. The goal of this project is to enable the helicopter to hover in place using a vision-based autopilot. The method consists of a single camera onboard the helicopter that is used to estimate the attitude and the position of the vehicle, except for the altitude, which is controlled manually from a joystick. These parameters are calculated using three dark colored targets mounted on a white wall. An algorithm processes the video frames to extract the necessary information, which is evaluated for errors and then passed to the control algorithm. The control signal for the helicopter is output via the audio port and driven through a custom-made circuit to eliminate noise and convert it to the necessary format. The results from flight tests are presented with the system’s advantages, limitations, and drawbacks discussed.

Utilizing UAV Payload Design by Undergraduate Researchers for Educational and Research Development

An undergraduate team consisting of mechanical and electrical engineering students at the University of North Dakota developed an electro-optical and un-cooled thermal infrared digital imaging remote sensing payload for an Unmanned Aerial Vehicle (UAV). The first iteration of the payload design began in the fall of 2005 and the inaugural flight tests took place at Camp Ripley, Minnesota, a National Guard facility, in the fall of 2006 with a corporate partner. The second iteration design with increased performance in object tracking and data processing is expected to fly in the summer of 2007. Payload development for integration into a UAV is a process that is not currently well defined by industrial practices or regulated by government. These processes are a significant part of the research being conducted in order to define the “best practices.” The emerging field of UAVs generates tremendous interest and serves to attract quality students into the research. As with many emerging technologies there are many new exciting developments, however, the fundamentals taught in core courses are still critical to the process and serve as the basis of the system. In this manner, the program stimulates innovative design while maintaining a solid connection to undergraduate courses and illustrates the importance of advanced courses. The payload development was guided by off-the-shelf components and software using a systems engineering methodology throughout the project. Many of the design and payload flight constraints were based on external factors, such as difficulties with access to airspace, weather-related delays, and ITAR restrictions on hardware. Overall, the research project continues to be a tremendous experiential learning activity for mechanical and electrical engineering students, as well as for the faculty members. The process has been extremely successful in enhancing the expertise in systems engineering and design in the students and developing the UAV payload design knowledge base and necessary infrastructure at the university.Copyright

Broad-band Viscoelastic Rotational Vibration Control for Remote Sensing Applications

The ability to eliminate, or effectively control, vibration in remote sensing applications is critical. Any perturbations of an imaging system are greatly magnified over the hundreds of kilometers from the orbiting space platform to the Earths surface. Space platforms, such as the International Space Station, are not as predictable or stable as many other spacecraft. Therefore, an effective vibration isolation and/or absorber system is needed that operates over a wide range of excitation frequencies. A passive system is also preferred to reduce the resources required, as well as to provide a reliable and self-contained system. To accomplish these goals, a vibration amplitude limiting system has been developed that uses both vibration isolation and absorber components. Viscoelastic structural elements that act as both a spring and a damper in a single element are implemented in the design. This configuration also demonstrates a favorable frequencydependent response and produces a system with improved dynamic behavior compared to conventional spring and damper designs. This rotation limiting vibration system has been designed and analyzed for use in digital remote sensing imaging. The transmissibility and the ground jitter associated with the system are determined. A summary of these results will be presented along with a comparison to a more conventional vibration isolation/absorber system.

Dynamic Separation Thresholds for a Small Airborne Sense and Avoid System

The replacement of distance-based thresholds with time-based thresholds that account for intruder performance is a necessary step in developing a useful sense-and-avoid system to be integrated into the national airspace system. The current conservative distance-based thresholds used in many sense-and-avoid systems on board UAS aircraft will either not adequately account for reduced maneuvering time when encountering high-speed aircraft or require such large thresholds to accommodate them that mission capability will be compromised. Using certain worst case assumptions regarding an intruder, the University of North Dakota’s Unmanned Aircraft Systems Engineering team (UASE) used turning flight geometry to implement a computationally inexpensive solution to the time-based question. The methodology and software in the loop results are presented illustrating the effectiveness of the time based avoidance threshold proposed.

Flight Testing of a Right-of-Way Compliant ADS-B-based Miniature Sense and Avoid System

A flight tested Right-of-Way (RoW) compliant algorithm has been developed as part of ongoing research efforts in the development of Airborne Sense and Avoid (ABSAA) technologies by the University of North Dakota Unmanned Aircraft Systems Engineering (UASE) team. This paper presents the results of development, implementation, and flight testing of a RoW algorithm during the summer of 2011 in a restricted airspace using a combination of varying intercept angles for the cases of a single intruder and dual intruders. These tests yielded positive results demonstrating the RoW compliance to enhance the UASE ABSAA system that was developed under a series of projects funded by the Department of Defense (DoD). The work presented implements the future NextGen National Airspace System (NAS) technologies and has the ability to incorporate multiple sensor streams into the decision space. An integral part of the NextGen NAS is the FAA’s final rule regarding “Automatic Dependent Surveillance-Broadcast (ADS-B) Out Performance Requirements to Support Air Traffic Control (ATC) Service,” which will propel forward the transition from a radar based system to a satellite driven system. This mandated technology allows for the development of a robust ABSAA system for Unmanned Aircraft Systems (UAS).

On-Board Visual Tracking with Unmanned Aircraft System (UAS)

This paper presents the development of a real time tracking algorithm that runs on a 1.2 GHz PC/104 computer on-board a small UAV. The algorithm uses zero mean normalized cross correlation to detect and locate an object in the image. A kalman filter is used to make the tracking algorithm computationally efficient. Object position in an image frame is predicted using the motion model and a search window, centered at the predicted position is generated. Object position is updated with the measurement from object detection. The detected position is sent to the motion controller to move the gimbal so that the object stays at the center of the image frame. Detection and tracking is autonomously carried out on the payload computer and the system is able to work in two different methods. The first method starts detecting and tracking using a stored image patch. The second method allows the operator on the ground to select the interest object for the UAV to track. The system is capable of re-detecting an object, in the event of tracking failure. Performance of the tracking system was verified both in the lab and on the field by mounting the payload on a vehicle and simulating a flight. Tests show that the system can detect and track a diverse set of objects in real time. Flight testing of the system will be conducted at the next available opportunity.

Flight Testing of an ADS-B-based Miniature 4D Sense and Avoid System for Small UAS

The University of North Dakota has been actively developing enabling airborne sense and avoid (SAA) technologies under a series of projects funded by the Department of Defense, and has selected the Automatic Dependent Surveillance – Broadcast (ADS-B) transceiver as one of its central components for cooperative aircraft mid-air collision avoidance. The system integrates a 4D (i.e., latitude, longitude, altitude, and time) SAA algorithm dependent on UAS dynamic performance factors. This paper presents the results of implementation and flight testing of a miniaturized collision avoidance system using ADS-B derived information. The flight test results that took place onboard a small UAS in the summer of 2010 demonstrate that a complex airborne SAA system utilizing an ADS-B transceiver can be integrated into a small UAS vehicle to improve safety and eventually allow integration of UAS vehicles into the U.S. National Airspace System (NAS).

Vision Based Neuro-Fuzzy Controller for a Two Axes Gimbal System with Small UAV

This paper presents the development of a vision-based neuro-fuzzy controller for a two axes gimbal system mounted on a small Unmanned Aerial Vehicle (UAV). The controller uses vision-based object detection as input and generates pan and tilt motion and velocity commands for the gimbal in order to keep the interest object at the center of the image frame. A readial basis function based neuro-fuzzy system and a learning algorithm is developed for the controller to address the dynamic and non-linear characteristics of the gimbal movement. The controller uses two separate, but identical radial basis function networks, one for pan and one for tilt motion of the gimbal. Each system is initialized with a fixed number of neurons that act as rules basis for the fuzzy inference system. The membership functions and rule strengths are then adjusted with the feedback from the visual tracking system. The controller is trained off-line until a desired error level is achieved. Training is then continued on-line to allow the system to accommodate air speed changes. The algorithm learns from the error computed from the detected position of the object in image frame and generates position and velocity commands for the gimbal movement. Several tests including lab tests and actual flight tests of the UAV have been carried out to demonstrate the effectiveness of the controller. Test results show that the controller is able to converge effectively and generate accurate position and velocity commands to keep the object at the center of the image frame.