Unmanned Aerial Systems (UAS) Traffic Management

aerospace
Unmanned Aerial Systems (UAS) Traffic Management (TOP2-237)
Safe and efficient UAS operations at lower altitude airspace
Overview
To enable significant commercial use of Unmanned Aerial Systems (UASs) within lower altitude airspace and airspace that does not interfere with regular National Airspace System (NAS) operations, an Unmanned Aerial Systems (UAS) Traffic Management (UTM) system is required. Such UTM system needs to support flight route adjustments of UASs that are encountering conflicts. NASA Ames Research Center has developed a traffic management system for Unmanned Aerial Systems (UASs) to maintain safe and efficient UAS operations. This novel technology can help provide solutions to these and other problems by implementing an Autonomous Situation Awareness Platform (ASAP) into UASs to allow them to autonomously resolve conflicts by UAS-to-UAS communications and onboard flight management systems, while maintaining integration with the National Airspace System. The technology enables the growth in civilian applications of UAS operations at lower altitudes by developing a UAS Traffic Management (UTM) system. There are a number of applications of UAS which includes goods and services delivery in urban, difficult terrain and rural areas, imaging and surveillance for agricultural, and utility management.

The Technology
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.
UTM Vision
Benefits
  • Incorporates Unmanned Aerial Systems (UASs) into the National Airspace System (NAS)
  • Provides UTM support in a geographically geo-fenced area on a continuous basis
  • UTM can be portable as-needed system or real-time continuous operation
  • Supports micro, small, and medium size UAS
  • Reliably provides communication, navigation, and surveillance below 10,000 ft.
  • Safe airspace operations by procedures and airspace design that keep UAS separated from other UAS and general aviation
  • Provides congestion management, route planning and rerouting, conflict avoidance, collision avoidance, terrain avoidance, obstacle avoidance, severe weather and wind avoidance services as needed based on needs of UASs operation and capability
  • Supports departure from and arrival into any location that is deemed safe
  • Supports operations at remote regions, and urban areas
  • Provides bi-directional communication mechanism

Applications
  • Wildfire mapping
  • Agriculture monitoring
  • Disaster management
  • Law enforcement
  • Telecommunication
  • Weather monitoring
  • Aerial imaging and mapping
  • Freight transport
  • Delivery of goods and services, like medical service delivery
  • Television news coverage, sporting events, movie making
  • Oil and gas exploration
Technology Details

aerospace
TOP2-237
ARC-17299-1
10,332,405
Similar Results
Urban Air Mobility
Near-Real Time Verification and Validation of Autonomous Flight Operations
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.
NASA UAV
Low Weight Flight Controller Design
Increasing demand for smaller UAVs (e.g., sometimes with wingspans on the order of six inches and weighing less than one pound) generated a need for much smaller flight and sensing equipment. NASA Langley's new sensing and flight control system for small UAVs includes both an active flight control board and an avionics sensor board. Together, these compare the status of the UAVs position, heading, and orientation with the pre-programmed data to determine and apply the flight control inputs needed to maintain the desired course. To satisfy the small form-factor system requirements, micro-electro-mechanical systems (MEMS) are used to realize the various flight control sensing devices. MEMS-based devices are commercially available single-chip devices that lend themselves to easy integration onto a circuit board. The system uses less energy than current systems, allowing solar panels planted on the vehicle to generate the systems power. While the lightweight technology was designed for smaller UAVs, the sensors could be distributed throughout larger UAVs, depending on the application.
Flying drone
Airborne Machine Learning Estimates for Local Winds and Kinematics
The MAchine learning ESTimations for uRban Operations (MAESTRO) system is a novel approach that couples commodity sensors with advanced algorithms to provide real-time onboard local wind and kinematics estimations to a vehicle's guidance and navigation system. Sensors and computations are integrated in a novel way to predict local winds and promote safe operations in dynamic urban regions where Global Positioning System/Global Navigation Satellite System (GPS/GNSS) and other network communications may be unavailable or are difficult to obtain when surrounded by tall buildings due to multi-path reflections and signal diffusion. The system can be implemented onboard an Unmanned Aerial Systems (UAS) and once airborne, the system does not require communication with an external data source or the GPS/GNSS. Estimations of the local winds (speed and direction) are created using inputs from onboard sensors that scan the local building environment. This information can then be used by the onboard guidance and navigation system to determine safe and energy-efficient trajectories for operations in urban and suburban settings. The technology is robust to dynamic environments, input noise, missing data, and other uncertainties, and has been demonstrated successfully in lab experiments and computer simulations.
Mitigating Risk in Commercial Aviation Operations
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.
The touch screen of the Electronic Flight Bag allows pilots to easily use TASAR.
Traffic Aware Strategic Aircrew Requests (TASAR)
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"
Stay up to date, follow NASA's Technology Transfer Program on:
facebook twitter linkedin youtube
Facebook Logo Twitter Logo Linkedin Logo Youtube Logo