Search

Actual air traffic data and weather information are utilized to evaluate an aircrafts flight-plan route and predict its trajectories for the climb, cruise, and descent phases. The dynamics for heading (the direction the aircraft nose is pointing) and airspeed are also modeled by the FACET software, while performance parameters, such as climb/descent rates and speeds and cruise speeds, can also be obtained from data tables. The resulting trajectories and traffic flow data are presented in a 3-D graphical user interface. The FACET software is modular and is written in the Java and C programming languages. Notable FACET applications include reroute conformance monitoring algorithms that have been implemented in one of the Federal Aviation Administrations nationally deployed, real-time operational systems.

NASA Ames has developed an Autonomous Situational Awareness Platform system for a UAS (ASAP-U), a traffic management system to incorporate Unmanned Aerial Systems (UASs) into the National Airspace System. The Autonomous Situational Awareness Platform (ASAP) is a system that combines existing navigation technology (both aviation and maritime) with new procedures to safely integrate Unmanned Aerial Systems (UASs) with other airspace vehicles. It uses a module called ASAP-U, which includes a transmitter, receivers, and various links to other UAS systems. The module collects global positioning system GPS coordinates and time from a satellite antenna, and this data is fed to the UAS's flight management system for navigation. The ASAP-U module autonomously and continuously sends UAS information via a radio frequency (RF) antenna using Self-Organized Time Division Multiple Access (SOTDMA) to prevent signal overlap. It also receives ASAP data from other aircraft. In case of transmission overload, priority is given to closer aircraft. Additionally, the module can receive weather data, navigational aid data, terrain data, and updates to the UAS flight plan. The collected data is relayed to the flight management system, which includes various databases and a navigation computer to calculate necessary flight plan modifications based on regulations, right-of-way rules, terrain, and geofencing. Conflicts are checked against databases, and if none are found, the flight plan is implemented. If conflicts arise, modifications can be made. The ASAP-U module continuously receives and transmits data, including UAS data and data from other aircraft, to detect conflicts with other aircraft, terrain, weather, and geofencing. Based on this information, the flight management system determines the need for course adjustments and the flight control system executes them for a safe flight route.

An approach to mitigating the creation of additional orbital debris is to remove the sources of future medium debris by actively removing large spent objects from congested orbits. NASA has introduced the ADRV, an efficient and effective solution to remove large debris from LEO such as spent rocket bodies and non-functional satellites. The concept yields a single use, low-cost, lightweight, high mass fraction vehicle that enables the specific removal of large orbital debris (1000 - 4000 kg mass, 200 - 2000 km altitude, and 20 98-degree inclination). The ADRV performs rendezvous, approach, and capture of non-cooperative tumbling debris objects, maneuvering of the mated vehicle, and controlled, targeted reposition or deorbit of the mated vehicle. Due to its small form factor, up to eight ADRVs can be launched in a single payload, enabling high impact orbital debris removal missions within the same inclination group. Three key technologies were developed to enable the ADRV: - 1) The spacecraft control system (SCS) is a guidance, navigation, and control system that provides vehicle control during all phases of a mission; - (2) The debris object characterization system (DOCS) characterizes movement and capture of non-cooperative targets; and - (3) The capture and release system (CARS) allows the vehicle to capture and mate with orbital debris targets. These technologies can improve the current state-of-the-art capabilities of automated rendezvous and docking technology significantly for debris objects with tumbling rates up to 25 degrees per second. This approach leverages decades of spaceflight experience while automating key mission areas to reduce cost and improve the likelihood of success.

As ever larger numbers of spacecraft seek to make use of Earth's limited orbital volume in increasingly dense orbital regimes, greater coordination becomes necessary to ensure these spacecraft are able to operate safely while avoiding physical collisions, radio-frequency interference, and other hazards. While efforts to date have focused on improving Space Situational Awareness (SSA) and enabling operator to operator coordination, there is growing recognition that a broader system for Space Traffic Management (STM) is necessary. The STM architecture forms the framework for an STM ecosystem, which enables the addition of third parties that can identify and fill niches by providing new, useful services. By making the STM functions available as services, the architecture reduces the amount of expertise that must be available internally within a particular organization, thereby reducing the barriers to operating in space and providing participants with the information necessary to behave responsibly. Operational support for collision avoidance, separation, etc., is managed through a decentralized architecture, rather than via a single centralized government-administered system. The STM system is based on the use of standardized Application Programming Interfaces (API) to allow easier interconnection and conceptual definition of roles to more easily allow suppliers with different capabilities to add value to the ecosystem. The architecture handles basic functions including registration, discovery, authentication of participants, and auditable tracking of data provenance and integrity. The technology is able to integrate data from multiple sources.

The goal of this computer vision software is to take the guesswork out of grapple operations aboard the ISS by providing a robotic arm operator with real-time pose estimation of the grapple fixtures relative to the robotic arms end effectors. To solve this Perspective-n-Point challenge, the software uses computer vision algorithms to determine alignment solutions between the position of the camera eyepoint with the position of the end effector as the borescope camera sensors are typically located several centimeters from their respective end effector grasping mechanisms. The software includes a machine learning component that uses a trained regional Convolutional Neural Network (r-CNN) to provide the capability to analyze a live camera feed to determine ISS fixture targets a robotic arm operator can interact with on orbit. This feature is intended to increase the grappling operational range of ISSs main robotic arm from a previous maximum of 0.5 meters for certain target types, to greater than 1.5 meters, while significantly reducing computation times for grasping operations. Industrial automation and robotics applications that rely on computer vision solutions may find value in this softwares capabilities. A wide range of emerging terrestrial robotic applications, outside of controlled environments, may also find value in the dynamic object recognition and state determination capabilities of this technology as successfully demonstrated by NASA on-orbit. This computer vision software is at a technology readiness level (TRL) 6, (system/sub-system model or prototype demonstration in an operational environment.), and the software is now available to license. Please note that NASA does not manufacture products itself for commercial sale.

Heterogeneous Spacecraft Networks address an emerging need, namely, the ability of satellites and other space-based assets to freely communicate with each other. While it appears that there has been no significant effort to date to address the application, emergence of such a solution is inevitable, given the rapidly-growing deployments of small satellites. These assets need to be able to communicate with each other and with global participants. Extending established global wireless network platforms like Wi-Fi and ZigBee to space-based assets will allow different satellite clusters to assist each other. For example, one cluster could provide images of the earths surface when another cluster is with out visibility at the needed time and location. More importantly, use of such common platforms will enable collaboration among individuals, institutions, and countries, each with limited assets of its own. Thus, allowing the incorporation of space-based assets into commercial wireless networks, and extending commercial communications into low Earth orbit satellites, access to satellite data will become ubiquitous.Similarly, some global networks will also benefit from the ability of a variety of nodes of different types to communicate with each other. One instance is in the emerging Internet of Things (IoT), where an enormous number of smart objects work together to provide customized solutions.

The novel Vision-based Approach and Landing System (VALS) provides Advanced Air Mobility (AAM) aircraft with an Alternative Position, Navigation, and Timing (APNT) solution for approach and landing without relying on GPS. VALS operates on multiple images obtained by the aircraft’s video camera as the aircraft performs its descent. In this system, a feature detection technique such as Hough circles and Harris corner detection is used to detect which portions of the image may have landmark features. These image areas are compared with a stored list of known landmarks to determine which features correspond to the known landmarks. The world coordinates of the best matched image landmarks are inputted into a Coplanar Pose from Orthography and Scaling with Iterations (COPOSIT) module to estimate the camera position relative to the landmark points, which yields an estimate of the position and orientation of the aircraft. The estimated aircraft position and orientation are fed into an extended Kalman filter to further refine the estimation of aircraft position, velocity, and orientation. Thus, the aircraft’s position, velocity, and orientation are determined without the use of GPS data or signals. Future work includes feeding the vision-based navigation data into the aircraft’s flight control system to facilitate aircraft landing.

The NASA software application developed under the TASAR project is called the Traffic Aware Planner (TAP). TAP automatically monitors for flight optimization opportunities in the form of lateral and/or vertical trajectory changes. Surveillance data of nearby aircraft, using ADS-B IN technology, are processed to evaluate and avoid possible conflicts resulting from requested changes in the trajectory. TAP also leverages real-time connectivity to external information sources, if available, of operational data relating to winds, weather, restricted airspace, etc., to produce the most acceptable and beneficial trajectory-change solutions available at the time. The software application is designed for installation on low-cost Electronic Flight Bags that provide read-only access to avionics data. The user interface is also compatible with the popular iPad. FAA certification and operational approval requirements are expected to be minimal for this non-safety-critical flight-efficiency application, reducing implementation cost and accelerating adoption by the airspace user community. Awarded "2016 NASA Software of the Year"

NASA's System for In-situ Defect (e.g., porosity, fiber waviness) Detection in Composites During Cure consists of an ultrasonic portable automated C-Scan system with an attached ultrasonic contact probe. This scanner is placed inside of an insulated vessel that protects the temperature-sensitive components of the scanner. A liquid nitrogen cooling systems keeps the interior of the vessel below 38°C. A motorized X-Y raster scanner is mounted inside an unsealed cooling container made of porous insulation boards with a cantilever scanning arm protruding out of the cooling container through a slot. The cooling container that houses the X-Y raster scanner is periodically cooled using a liquid nitrogen (LN2) delivery system. Flexible bellows in the slot opening of the box minimize heat transfer between the box and the external autoclave environment. The box and scanning arm are located on a precision cast tool plate. A thin layer of ultrasonic couplant is placed between the transducer and the tool plate. The composite parts are vacuum bagged on the other side of the tool plate and inspected. The scanning system inside of the vessel is connected to the controller outside of the autoclave. The system can provide A-scan, B-scan, and C-scan images of the composite panel at multiple times during the cure process. The in-situ system provides higher resolution data to find, characterize, and track defects during cure better than other cure monitoring techniques. In addition, this system also shows the through-thickness location of any composite manufacturing defects during cure with real-time localization and tracking. This has been demonstrated for both intentionally introduced porosity (i.e., trapped during layup) as well processing induced porosity (e.g., resulting from uneven pressure distribution on a part). The technology can be used as a non-destructive evaluation system when making composite parts in in an oven or an autoclave, including thermosets, thermoplastics, composite laminates, high-temperature resins, and ceramics.