Maarten Uijt de Haag

Design of an Electric Propulsion System for a Quadrotor Unmanned Aerial Vehicle

A quadrotor unmanned aerial vehicle has been developed at Ohio Universitys Avionics Engineering Center for use as a navigation sensor testbed. The quadrotor was designed with a 10 lb payload capacity, transforming what has typically been a lightweight airframe into a more robust platform. Specific design considerations included the characteristics of high-power brushless motors and electronic speed controllers, the variation of motor rotational losses with frequency, and the impact of heat dissipation within the battery packs. Simple feedback loops were sufficient to stabilize the platform. An accounting of the component efficiencies allowed for effective mission planning based on the desired payload. The quadrotor, with a demonstrated ability to lift up to 10.6 lb, provides a convenient way to flight-test new sensor technology.

Optimal thresholding for color images

Color image thresholding is a special case of color clustering which is commonly used for tasks such as object detection, region segmentation, enhancement, and target tracking. As compared to the three-dimensional (3-D) color clustering, thresholding is computationally more efficient for computer implementation and pipelined hardware realization. Traditionally, this method operates on a particular color component whose distribution possesses more prominent peaks than the other two color histograms. In this operation, it is expected that the histogram peaks represent meaningful object areas. However, the color component thresholding results are less reliable than those of 3-D clustering because the valuable information in the other two color components are ignored in region acceptance process. To improve the performance of thresholding, we describe a method that thresholds an input image three times on three different color components independently. The best thresholds are selected by optimizing the within-group variance or directed divergence measure for red, green, and blue distributions separately. The resultant three binary images are combined by means of a predicate logic function that makes use of a 3-input, 1-output majority logic gate. This enables 1-D thresholding mechanism to incorporate the information on all the color components in region acceptance process.

Application of laser range scanner based terrain referenced navigation systems for aircraft guidance

This paper discusses the various aspects of using airborne laser scanners (ALS) in terrain referenced navigation (TRN) systems. The paper addresses the system performance of these new ALS-based systems and compares their performance to traditional terrain referenced navigation systems based on radar altimeter and baro-altimeter sensors. The TRN system comparison also includes an inertial measurement unit (IMU) error sensitivity analysis and a discussion on the requirements imposed on the information content in the terrain elevation database by the remote sensor. The paper will use flight test data collected with Ohio Universitys DC-3Flying Laboratory in Braxton, WV to evaluate the various methodologies and analyses

Using X-band Weather Radar Measurements to Monitor the Integrity of Digital Elevation Models for Synthetic Vision Systems

Synthetic Vision Systems (SVS) provide pilots with displays of stored geo-spatial data representing terrain, obstacles, and cultural features. As comprehensive validation is impractical, these databases typically have no quantifiable level of integrity. Futher, updates to the databases may not be provided as changes occur. These issues limit the certification level and constrain the operational context of SVS for civil aviation. Previous work demonstrated the feasibility of using a real-time monitor to bound the integrity of Digital Elevation Models (DEMs) by using radar altimeter measurements during flight. This paper describes an extension of this concept to include X-band Weather Radar (WxR) measurements. This enables the monitor to detect additional classes of DEM errors and to reduce the exposure time associated with integrity threats. Feature extraction techniques are used along with a statistical assessment of similarity measures between the sensed and stored features that are detected. Recent flight-testing in the area around Juneau, Alaska Airport (JNU) has resulted in a comprehensive set of sensor data that is being used to assess the feasibility of the proposed monitor technology. Initial results of this assessment are presented.

Hazard tracking with integrity for surveillance applications

This paper presents a new aircraft traffic tracking algorithm for surveillance applications that integrates sensory information from multiple avionics sensors in an efficient manner and assesses the sensor consistency for integrity purposes. Measurements from various avionics sensors and ownship information are used to form a relative baseline vector. Once these relative baseline vectors are formed, they are integrated and estimated using an Interacting Multiple Model (IMM) filter, which has multiple Kalman filters with different dynamics models running in parallel and interacting with each other through an underlying Markov chain. Once the estimated baseline vector is obtained from the IMM filter, a series of integrity checks are performed. These include 1) the evaluation of the normalized residuals, and 2) the evaluation of the Autonomous Integrity Monitored Extrapolation (AIME) test statistic. Four sets of flight data are used to demonstrate the functionality of the algorithm, which include one set of simulated flight data based on several possible aircraft trajectories, and three sets of simulated data and real flight data from the RTCA DO-317 document.

Towards a seamless integration of awareness support and alerting systems: Why and how

In previous research, the requirement for increased awareness of (potential) future hazards has been addressed with an emphasis on the terrain hazard. The ease with which terrain awareness is obtained when using synthetic vision displays results from the fact that the pilot can see the terrain in a frame of reference in which extrapolation to the future situation is trivial, i.e. if the velocity vector points into terrain, the current direction of flight will lead to a loss of separation at some point. With an adequately designed synthetic vision display, the pilot also obtains information about the temporal distance to the hazard, yielding level 3 terrain awareness. In contrast, level 3 awareness of traffic hazards cannot simply be obtained by integrating a depiction of traffic in a synthetic vision display. Unlike the level 3 terrain awareness, which makes it almost impossible that a terrain alert comes as a surprise, an information gap still exists between traffic awareness support and the traffic alerting system. In this paper it is illustrated that trying to close this gap by focusing on traffic depiction is not the solution. To prevent a future loss of separation with traffic, knowing where the traffic currently is, is less important than determining whether, where, and when a loss of separation will occur. The information needed to answer these three questions can be provided through a visualization of the conflict space. The conflict probing concept can be used to achieve this, while at the same time integrating terrain, weather and traffic hazards. Given this potential, the conflict probing concept was selected as the approach to implement the human-machine interface of the Integrated Alerting and Notification function in the Integrated Intelligent Flight Deck research. In this paper, the rationale and the design of the visualization of conflict probes on the primary flight display, navigation display and vertical profile display are discussed.

Aerial vehicle navigation over unknown terrain environments using inertial measurements and dual airborne laser scanners or flash ladar

A precise navigation system for uninhabited or inhabited aerial vehicles is discussed in this paper. The navigational capability of an aerial vehicle must be robust and not easily influenced by external factors. Nowadays, many navigation systems rely somehow on the Global Positioning System (GPS), wherein the GPS signals may be rendered unusable due to unintentional interference caused by atmospheric effects, interference from communication equipment, as well as intentional jamming. The navigation method discussed in this paper integrates measurements from an Inertial Measurement Unit (IMU) with measurements from either two airborne laser scanners (ALS) or an airborne Flash LADAR (AFL) to provide autonomous navigational capability and a reliable alternative to GPS. The proposed system has applications in unknown or partially known terrain environments or it may also be used for autonomous landing systems in Lunar or Martian environments. Two approaches are described in this paper, one approach uses Dual Airborne Laser Scanners (DALS) (one pointing forward, the other pointing aft) and the other approach uses an AFL. Advantages and disadvantages of both approaches are discussed. The proposed navigation system uses strapdown IMU measurements to estimate the aerial vehicle position and attitude and to geo-reference the laser sensor data. It then uses the maps created from both the fore and aftpointing scanning LADARS or the consecutive Flash LADAR range-images to estimate systematic IMU errors such as position and velocity drifts. The proposed navigation algorithm is evaluated using flight test data from Ohio Universitys DC3 aircraft and synthesized ALS and AFL measurements. Initial results are observed to achieve meter level accuracies in the systems position drift performance.

An X-band radar terrain feature detection method for low-altitude SVS operations and calibration using lidar

To enable safe use of Synthetic Vision Systems at low altitudes, real-time range-to-terrain measurements may be required to ensure the integrity of terrain models stored in the system. This paper reviews and extends previous work describing the application of x-band radar to terrain model integrity monitoring. A method of terrain feature extraction and a transformation of the features to a common reference domain are proposed. Expected error distributions for the extracted features are required to establish appropriate thresholds whereby a consistency-checking function can trigger an alert. A calibration-based approach is presented that can be used to obtain these distributions. To verify the approach, NASAs DC-8 airborne science platform was used to collect data from two mapping sensors. An Airborne Laser Terrain Mapping (ALTM) sensor was installed in the cargo bay of the DC-8. After processing, the ALTM produced a reference terrain model with a vertical accuracy of less than one meter. Also installed was a commercial-off-the-shelf x-band radar in the nose radome of the DC-8. Although primarily designed to measure precipitation, the radar also provides estimates of terrain reflectivity at low altitudes. Using the ALTM data as the reference, errors in features extracted from the radar are estimated. A method to estimate errors in features extracted from the terrain model is also presented.

A simulation environment for evaluation of Integrated Alerting and Notification (IAN) concepts

In order to pursue solutions that simultaneously increase the flight crews ability to avoid, detect, and recover from unexpected events while also providing countermeasures to pilot error, an onboard Hazard and Integrity Monitor (HIM) function as well as an Integrated Alerting and Notification (IAN) function have previously been proposed to detect and assess hazards, and issue integrated and prioritized alerts and notifications to the flight crew. The objective of these functions is to mitigate hazards introduced by the operators, the application of automation, and the environment. This paper presents a simulation environment developed for verification and validation of the HIM and IAN concepts. This simulation environment models aircraft communication, navigation, surveillance, and health reporting systems and sensors, along with their respective error models. The environment allows for test and evaluation of HIM and IAN concepts under varying sensor and data link configurations and can easily be interface with human-in-the-loop simulators as well. Because of its flexibility of the onboard avionics configurations, this simulation environment can also be used to test other new avionics sensors and algorithms. This paper will discuss the functionality of this simulation environment including avionics properties, data structures, interfaces, and operational modes. Finally, this paper will describe an experiment being prepared on the Research Flight Deck (RFD) at the National Aeronautics and Space Administration (NASA) Langley Research Center (LaRC) that will use this simulation environment in a human-in-the-loop study.

Integration of 3D and 2D imaging data for assured navigation in unknown environments

This paper discusses the development of a novel navigation method that integrates three-dimensional (3D) point cloud data, two-dimensional (2D) digital camera data, and data from an Inertial Measurement Unit (IMU). The target application is to provide an accurate position and attitude determination of unmanned aerial vehicles (UAV) or autonomous ground vehicles (AGV) in any urban or indoor environments, during any scenario. In some urban and indoor environments, GPS signals are attainable and usable for these target applications, but this is not always the case. GPS position capability may not only be unavailable due to shadowing, significant signal attenuation or multipath, but also due to intentional denial or deception. In these scenarios where GPS is not a viable, or reliable option, a system must be developed that compliments GPS and works in the environments where GPS encounters problems. The proposed algorithm is an effort to show one possible method that a complementary system to GPS could use. It extracts key features such as planar surfaces, lines, corners, and points from both the 3D (point-cloud) and 2D (intensity) imagery. Consecutive observations of corresponding features in the 3D and 2D image frames are then used to compute estimates of position and orientation changes. Since the use of 3D image features for positioning suffers from limited feature observability resulting in deteriorated position accuracies, and the 2D imagery suffers from an unknown depth when estimating the pose from consecutive image frames, it is expected that the integration of both data sets will alleviate the problems with the individual methods resulting in a position and attitude determination procedure with a high level of assurance. An Inertial Measurement Unit (IMU) is used to set up the tracking gates necessary to perform data association of the features in consecutive frames. Finally, the position and orientation change estimates can be used to correct for and mitigate the IMU drift errors.