Universal Data Compiler for Drone, Sensor, and System Data Integration

The DLC platform is composed of three key components: a NASA-designed field programmable gate array (FPGA) board, a NASA-designed multiprocessor on-a-chip (MPSoC) board, and a proprietary datapath that links the boards to available inputs and outputs to enable high-bandwidth data collection and processing. The inertial measurement unit (IMU), camera, Navigation Doppler Lidar (NDL), and Hazard Detection Lidar (HDL) navigation sensors (depicted in the diagram below) are connected to the DLC’s FPGA board. The datapath on this board consists of high-speed serial interfaces for each sensor, which accept the sensor data as input and converts the output to an AXI stream format. The sensor streams are multiplexed into an AXI stream which is then formatted for input to a XAUI high speed serial interface. This interface sends the data to the MPSoC Board, where it is converted back from the XAUI format to a combined AXI stream, and demultiplexed back into individual sensor AXI streams. These AXI streams are then inputted into respective DMA interfaces that provide an interface to the DDRAM on the MPSoC board. This architecture enables real-time high-bandwidth data collection and processing by preserving the MPSoC’s full ability. This sensor datapath architecture may have other potential applications in aerospace and defense, transportation (e.g., autonomous driving), medical, research, and automation/control markets where it could serve as a key component in a high-performance computing platform and/or critical embedded system for integrating, processing, and analyzing large volumes of data in real-time.

NASA’s newly developed software leverages flight operations data (e.g., SWIM Terminal Data Distribution System (STDDS) information), and with it, can predict aviation related risks, such as unstable approaches of flights. To do this, the software inputs the complex, multi-source STDDS data, and outputs novel prediction and outcome information. The software converts the relatively inaccessible SWIM data from its native format that is not data science friendly into a format easily readable by most programs. The converted, model friendly data are then input into machine learning algorithms to enable risk prediction capabilities. The backend software sends the machine learning algorithm results to the front end software to display the results in appropriate user interfaces. These user interfaces can be deployed on different platforms including mobile phones and desktop computers and efficiently update models based on changes in the data. To allow for visualization, the software uses a commercially available mapping API. The data are visualized in several different ways, including a heat map layer that shows the risk score, with higher risk in areas of higher flight density, a polyline layer, which shows flight paths, and markers that can indicate a flight’s location in real time, among other things. The related patent is now available to license. Please note that NASA does not manufacturer products itself for commercial sale.

NASA Armstrong collaborated with the U.S. Air Force to develop algorithms that interpret highly encoded large area terrain maps with geographically user-defined error tolerances. A key feature of the software is its ability to locally decode and render DTMs in real time for a high-performance airplane that may need automatic course correction due to unexpected and dynamic events. Armstrong researchers are integrating the algorithms into a Global Elevation Data Adaptive Compression System (GEDACS) software package, which will enable customized maps from a variety of data sources. How It Works The DTM software achieves its high performance encoding and decoding processes using a unique combination of regular and semi-regular geometric tiling for optimal rendering of a requested map. This tiling allows the software to retain important slope information and continuously and accurately represent the terrain. Maps and decoding logic are integrated into an aircraft's existing onboard computing environment and can operate on a mobile device, an EFB, or flight control and avionics computer systems. Users can adjust the DTM encoding routines and error tolerances to suit evolving platform and mission requirements. Maps can be tailored to flight profiles of a wide range of aircraft, including fighter jets, UAVs, and general aviation aircraft. The DTM and GEDACS software enable the encoding of global digital terrain data into a file size small enough to fit onto a tablet or other handheld/mobile device for next-generation ground collision avoidance. With improved digital terrain data, aircraft could attain better performance. The system monitors the ground approach and an aircraft's ability to maneuver by predicting several multidirectional escape trajectories, a feature that will be particularly advantageous to general aviation aircraft. Why It Is Better Conventional DTM encoding techniques used aboard high-performance aircraft typically achieve relatively low encoding process ratios. Also, the computational complexity of the decoding process can be high, making them unsuitable for the real-time constrained computing environments of high-performance aircraft. Implementation costs are also often prohibitive for general aviation aircraft. This software achieves its high encoding process ratio by intelligently interpreting its maps rather than requiring absolute retention of all data. For example, the DTM software notes the perimeter and depth of a mining pit but ignores contours that are irrelevant based on the climb and turn performance of a particular aircraft and therefore does not waste valuable computational resources. Through this type of intelligent processing, the software eliminates the need to maintain absolute retention of all data and achieves a much higher encoding process ratio than conventional terrain-mapping software. The resulting exceptional encoding process allows users to store a larger library of DTMs in one place, enabling comprehensive map coverage at all times. Additionally, the ability to selectively tailor resolution enables high-fidelity sections of terrain data to be incorporated seamlessly into a map.

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.

NASA's Extensible Traffic Management (xTM) system allows for distributed management of the airspace where disparate entities collaborate to maintain a safe and accessible environment. This digital ecosystem relies on a common data generation and transfer framework enabled by well-defined data collection requirements, algorithms, protocols, and Application Programming Interfaces (APIs). The key components in this new paradigm are: Data Standardization: Defines the list of data attributes/variables that are required to inform and safely perform the intended missions and operations. Automated Real Time And/or Post-Flight Data Verification Process: Verifies system criteria, specifications, and data quality requirements using predefined, rule-based, or human-in-the-loop verification. Autonomous Evolving Real Time And/or Post-Flight Data Validation Process: Validates data integrity, quantity, and quality for audit, oversight, and optimization. The verification and validation process determines whether an operation’s performance, conformance, and compliance are within known variation. The technology can verify thousands of flight operations in near-real time or post flight in the span of a few minutes, depending on networking and computing capacity. In contrast, manual processing would have required hours, if not days, for a team of 2-3 experts to review an individual flight.