Effects of magnification and visual accommodation on aimpoint estimation in simulated landings with real and virtual image displays

Twenty professional pilots observed a computer-generated airport scene during simulated autopilot-coupled night landing approaches and at two points (20 sec and 10 sec before touchdown) judged whether the airplane would undershoot or overshoot the aimpoint. Visual accommodation was continuously measured using an automatic infrared optometer. Experimental variables included approach slope angle, display magnification, visual focus demand (using ophthalmic lenses), and presentation of the display as either a real (direct view) or a virtual (collimated) image. Aimpoint judgments shifted predictably with actual approach slope and display magnification. Both pilot judgments and measured accommodation interacted with focus demand with real-image displays but not with virtual-image displays. With either type of display, measured accommodation lagged far behind focus demand and was reliably less responsive to the virtual images. Pilot judgments shifted dramatically from an overwhelming perceived-overshoot bias 20 sec before touchdown to a reliable undershoot bias 10 sec later

Airborne Wind Shear Detection and Warning Systems. Second Combined Manufacturers' and Technologists' Conference, part 1

The Second Combined Manufacturers' and Technologists' Conference hosted jointly by NASA Langley (LaRC) and the Federal Aviation Administration (FAA) was held in Williamsburg, Virginia, on October 18 to 20, 1988. The purpose of the meeting was to transfer significant, ongoing results gained during the second year of the joint NASA/FAA Airborne Wind Shear Program to the technical industry and to pose problems of current concern to the combined group. It also provided a forum for manufacturers to review forward-look technology concepts and for technologists to gain an understanding of the problems encountered by the manufacturers during the development of airborne equipment and the FAA certification requirements

Human wayfinding and navigation in a large-scale environment : cognitive map development and wayfinding strategies

In a large scale environment humans rely on their mental representations —cognitive maps— to solve navigational problems. To approach the understanding of how humans acquire, process, and utilize information from the environment, three groups of participants in this study performed several experiments associated with finding their way in a previously unknown environment. Experimental tasks included route retracing, pointing to previously visited locations, and a questionnaire regarding wayfinding strategies and cognitive map development. Each of three groups of participants was in one of three unique conditions: 1. learning and retracing with navigational landmarks indicating right and left turns at decision points; 2. during route retracing only generic landmarks were present at decision points (landmarks indicating left and right were present during learning but replaced during retracing); and 3. no landmarks were present during route retracing (landmarks indicating left and right were present during learning but removed before retracing started). Results supported the hypothesis that during the initial stages of visiting an unknown environment we build metric knowledge together with non-metric knowledge associated with the broad categories of landmark and route knowledge. In addition, the environment plays an important role in wayfinding performance and that characteristics of the environment contribute differently to the development of our cognitive map. Last but not least, the strategies humans use to solve wayfinding problems in a novel environment are not based on an individual type of environmental knowledge; in fact, we switch between different types of environmental knowledge when necessary. Shifting between strategies appears to be from more familiar environmental knowledge to less familiar knowledge. In particular, participants from group 3 (no landmarks during the retracing period) were more likely to walk off-route during retracing but exhibited more accurate metric knowledge of the environment. Based on the results of this experiment, they combined route- and survey-based strategies in wayfinding and switched from the most familiar knowledge to a less familiar strategy

Preliminary study and design of the avionics system for an eVTOL aircraft.

The project consists of the study, creation, implementation, and development of the avionics system of an electric Vertical Take-Off and Landing (eVTOL) airplane for an ongoing project from the company ONAEROSPACE. The plane is intended to be for 7 passengers and 1 pilot, with a maximum range of 1000+ km. The fuselage will be formed of carbon fiber composite to reduce weight and it will use electric motors powered by batteries. The avionics system will provide the aircraft with communication and navigation systems, an autonomous Take-Off (T/O) and landing system, as well as the development of cockpit management systems. This document is divided into two parts. The first part begins with the study of all the necessary tools for communication and navigation systems. That means all compulsory antennas and sensors to obtain information about the surroundings (weather, obstacles, other planes¿). The intern communication network to send data from these sensors and antennas to main flight management systems is also studied in this first part. The second part of the project is dedicated to cabin cockpit systems and the study for the future development of autonomous systems. The cabin will have a full-glass cockpit, with touchable screens and an intelligent voice assistant. It will be very ergonomic and simple, with a lot of space in the cabin. In order to have an idea of the cost of the implementation of all the systems for the aircraft, a weight and cost estimation analysis are done at the end of each section. The last part of the project consists of the study of the design of a virtual intelligent voice assistant and the implementation of autonomous systems. Nowadays, the virtual intelligent voice assistant is an artificial intelligence system that works as a pilot monitoring system which assists the pilot in order to decrease the pilot¿s workload. The future idea is that the pilot could tell commands to the voice assistant and do nothing with the hands, just control that everything works correctly. Regarding the autonomous system, the conclusion is that with the existent technology is not possible today. Nevertheless, in the future, when fully autonomous aircraft are possible, the idea is that although being fully autonomous, the pilot can take the control of the aircraft at any moment.OutgoingObjectius de Desenvolupament Sostenible::9 - Indústria, Innovació i InfraestructuraObjectius de Desenvolupament Sostenible::11 - Ciutats i Comunitats Sostenible

SIGS: Synthetic Imagery Generating Software for the development and evaluation of vision-based sense-and-avoid systems

Unmanned Aerial Systems (UASs) have recently become a versatile platform for many civilian applications including inspection, surveillance and mapping. Sense-and-Avoid systems are essential for the autonomous safe operation of these systems in non-segregated airspaces. Vision-based Sense-and-Avoid systems are preferred to other alternatives as their price, physical dimensions and weight are more suitable for small and medium-sized UASs, but obtaining real flight imagery of potential collision scenarios is hard and dangerous, which complicates the development of Vision-based detection and tracking algorithms. For this purpose, user-friendly software for synthetic imagery generation has been developed, allowing to blend user-defined flight imagery of a simulated aircraft with real flight scenario images to produce realistic images with ground truth annotations. These are extremely useful for the development and benchmarking of Vision-based detection and tracking algorithms at a much lower cost and risk. An image processing algorithm has also been developed for automatic detection of the occlusions caused by certain parts of the UAV which carries the camera. The detected occlusions can later be used by our software to simulate the occlusions due to the UAV that would appear in a real flight with the same camera setup. Additionally this algorithm could be used to mask out pixels which do not contain relevant information of the scene for the visual detection, making the image search process more efficient. Finally an application example of the imagery obtained with our software for the benchmarking of a state-of-art visual tracker is presented

Three-D multilateration: A precision geodetic measurement system

A technique of satellite geodesy for determining the relative three dimensional coordinates of ground stations within one centimeter over baselines of 20 to 10,000 kilometers is discussed. The system is referred to as 3-D Multilateration and has applications in earthquake hazard assessment, precision surveying, plate tectonics, and orbital mechanics. The accuracy is obtained by using pulsed lasers to obtain simultaneous slant ranges between several ground stations and a moving retroreflector with known trajectory for aiming the lasers

A model-based approach for detection of runways and other objects in image sequences acquired using an on-board camera

This research was initiated as a part of the Advanced Sensor and Imaging System Technology (ASSIST) program at NASA Langley Research Center. The primary goal of this research is the development of image analysis algorithms for the detection of runways and other objects using an on-board camera. Initial effort was concentrated on images acquired using a passive millimeter wave (PMMW) sensor. The images obtained using PMMW sensors under poor visibility conditions due to atmospheric fog are characterized by very low spatial resolution but good image contrast compared to those images obtained using sensors operating in the visible spectrum. Algorithms developed for analyzing these images using a model of the runway and other objects are described in Part 1 of this report. Experimental verification of these algorithms was limited to a sequence of images simulated from a single frame of PMMW image. Subsequent development and evaluation of algorithms was done using video image sequences. These images have better spatial and temporal resolution compared to PMMW images. Algorithms for reliable recognition of runways and accurate estimation of spatial position of stationary objects on the ground have been developed and evaluated using several image sequences. These algorithms are described in Part 2 of this report. A list of all publications resulting from this work is also included

A Partially Randomized Approach to Trajectory Planning and Optimization for Mobile Robots with Flat Dynamics

Motion planning problems are characterized by huge search spaces and complex obstacle structures with no concise mathematical expression. The fixed-wing airplane application considered in this thesis adds differential constraints and point-wise bounds, i. e. an infinite number of equality and inequality constraints. An optimal trajectory planning approach is presented, based on the randomized Rapidly-exploring Random Trees framework (RRT*). The local planner relies on differential flatness of the equations of motion to obtain tree branch candidates that automatically satisfy the differential constraints. Flat output trajectories, in this case equivalent to the airplane's flight path, are designed using Bézier curves. Segment feasibility in terms of point-wise inequality constraints is tested by an indicator integral, which is evaluated alongside the segment cost functional. Although the RRT* guarantees optimality in the limit of infinite planning time, it is argued by intuition and experimentation that convergence is not approached at a practically useful rate. Therefore, the randomized planner is augmented by a deterministic variational optimization technique. To this end, the optimal planning task is formulated as a semi-infinite optimization problem, using the intermediate result of the RRT(*) as an initial guess. The proposed optimization algorithm follows the feasible flavor of the primal-dual interior point paradigm. Discretization of functional (infinite) constraints is deferred to the linear subproblems, where it is realized implicitly by numeric quadrature. An inherent numerical ill-conditioning of the method is circumvented by a reduction-like approach, which tracks active constraint locations by introducing new problem variables. Obstacle avoidance is achieved by extending the line search procedure and dynamically adding obstacle-awareness constraints to the problem formulation. Experimental evaluation confirms that the hybrid approach is practically feasible and does indeed outperform RRT*'s built-in optimization mechanism, but the computational burden is still significant.Bewegungsplanungsaufgaben sind typischerweise gekennzeichnet durch umfangreiche Suchräume, deren vollständige Exploration nicht praktikabel ist, sowie durch unstrukturierte Hindernisse, für die nur selten eine geschlossene mathematische Beschreibung existiert. Bei der in dieser Arbeit betrachteten Anwendung auf Flächenflugzeuge kommen differentielle Randbedingungen und beschränkte Systemgrößen erschwerend hinzu. Der vorgestellte Ansatz zur optimalen Trajektorienplanung basiert auf dem Rapidly-exploring Random Trees-Algorithmus (RRT*), welcher die Suchraumkomplexität durch Randomisierung beherrschbar macht. Der spezifische Beitrag ist eine Realisierung des lokalen Planers zur Generierung der Äste des Suchbaums. Dieser erfordert ein flaches Bewegungsmodell, sodass differentielle Randbedingungen automatisch erfüllt sind. Die Trajektorien des flachen Ausgangs, welche im betrachteten Beispiel der Flugbahn entsprechen, werden mittels Bézier-Kurven entworfen. Die Einhaltung der Ungleichungsnebenbedingungen wird durch ein Indikator-Integral überprüft, welches sich mit wenig Zusatzaufwand parallel zum Kostenfunktional berechnen lässt. Zwar konvergiert der RRT*-Algorithmus (im probabilistischen Sinne) zu einer optimalen Lösung, jedoch ist die Konvergenzrate aus praktischer Sicht unbrauchbar langsam. Es ist daher naheliegend, den Planer durch ein gradientenbasiertes lokales Optimierungsverfahren mit besseren Konvergenzeigenschaften zu unterstützen. Hierzu wird die aktuelle Zwischenlösung des Planers als Initialschätzung für ein kompatibles semi-infinites Optimierungsproblem verwendet. Der vorgeschlagene Optimierungsalgorithmus erweitert das verbreitete innere-Punkte-Konzept (primal dual interior point method) auf semi-infinite Probleme. Eine explizite Diskretisierung der funktionalen Ungleichungsnebenbedingungen ist nicht erforderlich, denn diese erfolgt implizit durch eine numerische Integralauswertung im Rahmen der linearen Teilprobleme. Da die Methode an Stellen aktiver Nebenbedingungen nicht wohldefiniert ist, kommt zusätzlich eine Variante des Reduktions-Ansatzes zum Einsatz, bei welcher der Vektor der Optimierungsvariablen um die (endliche) Menge der aktiven Indizes erweitert wird. Weiterhin wurde eine Kollisionsvermeidung integriert, die in den Teilschritt der Liniensuche eingreift und die Problemformulierung dynamisch um Randbedingungen zur lokalen Berücksichtigung von Hindernissen erweitert. Experimentelle Untersuchungen bestätigen, dass die Ergebnisse des hybriden Ansatzes aus RRT(*) und numerischem Optimierungsverfahren der klassischen RRT*-basierten Trajektorienoptimierung überlegen sind. Der erforderliche Rechenaufwand ist zwar beträchtlich, aber unter realistischen Bedingungen praktisch beherrschbar