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At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

UAS Integration into the NAS: HSI Display Evaluation Overview

This is a HSI (Horizontal Situation Indicator) display evaluation overview presented to the RTCA (Radio Technical Commission for Aeronautics) SC-228 Detect and Avoid Working group. The goal of the presentation is to provide data on the effect of various Detect and Avoid (DAA) display and guidance features with respect to pilot performance of the self-separation function in order to determine the minimum information requirements for DAA displays.

Well Clear↗
NASA NTRS2016

Defining the Collision Avoidance Region for DAA Systems

Unmanned aircraft systems (UAS) will be required to equip with a detect-­‐and-­‐avoid (DAA) system in order to satisfy the federal aviation regulations to maintain well clear of other aircraft, some of which may be equipped with a Traffic Collision Avoidance System (TCAS) to mitigate the possibility of mid-­‐air collisions. As such, the minimum operational performance standards (MOPS) for UAS DAA systems are being designed with TCAS interoperability in mind by a group of industry, government, and academic institutions named RTCA Special Committee-228 (SC-228). This document will discuss the development of the spatial-­‐temporal volume known as the collision avoidance region in which the DAA system is not allowed to provide vertical guidance to maintain or regain DAA well clear that could conflict with resolution advisories (RAs) issued by the intruder aircraft's TCAS system. Three collision avoidance region definition candidates were developed based on the existing TCAS RA and DAA alerting definitions. They were evaluated against each other in terms of their interoperability with TCAS RAs and DAA alerts in an unmitigated factorial encounter analysis of 1.3 million simulated pairs.

collision avoidance region↗
DOE OSTI2016

Compiler-Directed Soft Error Detection and Recovery to Avoid DUE and SDC via Tail-DMR

This article presents Clover, a compiler-directed soft error detection and recovery scheme for lightweight soft error resilience. The compiler carefully generates soft-error-tolerant code based on idempotent processing without explicit checkpoints. During program execution, Clover relies on a small number of acoustic wave detectors deployed in the processor to identify soft errors by sensing the wave made by a particle strike. To cope with DUEs (detected unrecoverable errors) caused by the sensing latency of error detection, Clover leverages a novel selective instruction duplication technique called tail-DMR (dual modular redundancy) that provides a region-level error containment. Once a soft error is detected by either the sensors or the tail-DMR, Clover takes care of the error as in the case of exception handling. To recover from the error, Clover simply redirects program control to the beginning of the code region where the error is detected. The experimental results demonstrate that the average runtime overhead is only 26%, which is a 75% reduction compared to that of the state-of-the-art soft error resilience technique. In addition, this article evaluates an alternative technique called tail-wait, comparing it to Clover. According to the evaluation with the different processor configurations and the various error detection latencies, Clover turns out to be a superior technique, achieving 1.06 to 3.49× speedup over the tail-wait.

Computer Science↗
DOE OSTI2021

Programmable intrusion detection for distributed energy resources in cyber–physical networked microgrids

We present a programmable intrusion detection method is presented to identify the malicious attacks to distributed energy resources (DERs) in the cyber-physical networked microgrids. The proposed method injects small programmable signals into the system and uses the response to identify abnormal conditions. Because of the low or even zero inertia induced by integrations of DER power-electronic-interfaces, microgrids have very limited resilience capability; and thus, being sensitive to attacks. One microgrid's malfunction caused by attacks can easily propagate to its neighboring systems when several microgrids are connected, leading to catastrophic electricity supply failures. Through the presented method, malicious intrusions can be effectively detected, located, and defended for securing microgrids. Theoretical derivations are provided to define the programmable detection rules. The detection rule is easy and flexible to update, making it difficult for attack actors to gain the knowledge of the detection rules, in order to avoid being detected. Numerical results on a cyber-physical networked microgrids system show that the proposed method is effective and efficient in precisely locating intrusion attacks to the microgrids system.

42 ENGINEERING↗
NASA NTRS2018

A Hazard Detection Sensor for Landing on Europa

Based on the limited imagery available, the surface topography of Jupiter’s icy moon Europa is expected to be hazardous for robotic landers. The Europa Lander Concept Pre-Project Team has therefore concluded that onboard hazard detection (and avoidance) using an imaging light detection and ranging (lidar) system is an enabling technology. In this paper we describe the challenges, requirements, technical solution space, and our maturation strategy to advance lidar technology for a Europa Lander mission concept to TRL 6 by 2021. JPL is confident that the resulting technology will be of value to a wide range of lunar and planetary landing missions.

Katake, Anup↗
NASA NTRS2019

Unmanned Aircraft Systems (UAS) Integration in the National Airspace System (NAS) Project

Phase 1 of the UAS-NAS (Unmanned Aircraft Systems-National Airspace System) project focused on MOPS (Minimum Operational Performance Standards) development for large UAS transitioning through Class D, E, and G airspace. Phase 2 activities are currently focused on extended UAS operations in Class D, E, and G airspace, as well as sensors and architectures that will enable DAA (Detect and Avoid) equipment to be installed on a wider range of UAS. Encounters with non-cooperative intruders in low altitude airspace under 10,000 feet will be explored for UAS with a low Size, Weight, and Power (SWaP) radar. New sensors for detecting non-cooperative intruder aircraft will have a more limited detection range and field of regard; therefore, the analysis of non-cooperative encounter geometries may help in developing the requirements for an onboard low SWaP radar (and electro-optical sensor). The current simulation, led by HSI (Human Systems Integration Division at NASA Ames Research Center) as part of the DAA subproject, will support efforts toward developing a modified DAA Well Clear (DWC) definition that would be more appropriate for UAS equipped with limited surveillance and aircraft performance capabilities compared to the Phase 1 DAA system. The findings will inform Phase 2 display requirements for alternative surveillance use cases.

Collision Avoidance↗
NASA NTRS2018

UAS-NAS NASA 870 Ikhana UAS No Chase COA (NCC) Flights, Flight Test Report

This document is a flight test report from the operational perspective for the No Chase Certificate of Waiver or Authorization (COA) flights, or NCC flights, a major milestone of the Unmanned Aircraft Systems (UAS) Integration in the National Airspace System (NAS) project. Discussions of a demonstration event began as early as 2014 and the actual flight of the Ikhana UAS into the NAS without a safety chase vehicle in Class A, E, and D airspace was accomplished on 12 June, 2018. The major goal of this flight was to demonstrate an alternate means of compliance to the see and avoid regulations for a UAS using Detect and Avoid (DAA) technology. Participants in this flight activity and planning included the National Aeronautics and Space Administration (NASA) Ames Research Center, NASA Armstrong Flight Research Center, General Atomics Aeronautical Systems, Inc. (GA-ASI), Honeywell International, Inc., and the Federal Aviation Administration (FAA).

Marston, Mike↗
NASA NTRS2016

Flight Test 4 Preliminary Results: NASA Ames SSI

Realization of the expected proliferation of Unmanned Aircraft System (UAS) operations in the National Airspace System (NAS) depends on the development and validation of performance standards for UAS Detect and Avoid (DAA) Systems. The RTCA Special Committee 228 is charged with leading the development of draft Minimum Operational Performance Standards (MOPS) for UAS DAA Systems. NASA, as a participating member of RTCA SC-228 is committed to supporting the development and validation of draft requirements as well as the safety substantiation and end-to-end assessment of DAA system performance. The Unmanned Aircraft System (UAS) Integration into the National Airspace System (NAS) Project conducted flight test program, referred to as Flight Test 4, at Armstrong Flight Research Center from April -June 2016. Part of the test flights were dedicated to the NASA Ames-developed Detect and Avoid (DAA) System referred to as JADEM (Java Architecture for DAA Extensibility and Modeling). The encounter scenarios, which involved NASA's Ikhana UAS and a manned intruder aircraft, were designed to collect data on DAA system performance in real-world conditions and uncertainties with four different surveillance sensor systems. Flight test 4 has four objectives: (1) validate DAA requirements in stressing cases that drive MOPS requirements, including: high-speed cooperative intruder, low-speed non-cooperative intruder, high vertical closure rate encounter, and Mode CS-only intruder (i.e. without ADS-B), (2) validate TCASDAA alerting and guidance interoperability concept in the presence of realistic sensor, tracking and navigational errors and in multiple-intruder encounters against both cooperative and non-cooperative intruders, (3) validate Well Clear Recovery guidance in the presence of realistic sensor, tracking and navigational errors, and (4) validate DAA alerting and guidance requirements in the presence of realistic sensor, tracking and navigational errors. The results will be presented at RTCA Special Committee 228 in support of final verification and validation of the DAA MOPS.

Detect-And-Avoid (DAA)↗
NASA NTRS2018

Unmanned Aerial Systems (UAS) Integration into the National Airspace System (NAS)

NASA's Unmanned Aerial Systems (UAS) integration into the National Air Space (NAS) project has been working closely with the FAA and RTCA Special Committee 228 to identify and break down barriers to UAS integration. A focus of this work is on detect and avoid (DAA) technologies. A pilot has responsibility to see and avoid other aircraft and to remain "well clear," using their best judgment (Federal Aviation Regulations (FAR) Sec. 91.113). For UAS to perform this function, the see function is replaced by sensors to detect the other aircraft. Secondly, the pilot judgment of well clear has to be replaced by a mathematical expression. For Phase 1 of this effort, a well clear violation was defined if all three of these conditions are true: a) the horizontal clearance is less than 4000 ft., and b) the vertical clearance is less than 450 ft., and c) the time to loss of well clear is less than 35 seconds. This definition was developed with a great deal of community input and testing to ensure interoperability with Air Traffic Control (ATC) and pilots of manned aircraft. Appropriate guidance, alerting and displays were developed to allow UAS, with the appropriate sensors, to effectively maintain well clear. This work contributed to FAA Technical Standard Orders: TSO-C211, Detect and Avoid and TSO-C212, ATAR for Traffic Surveillance. Phase 2 of this work extends the operational environment to include the terminal area and lesser capable aircraft that might not have the payload capability to carry the RADAR defined in Phase 1. This session reports on work from Phase 1 and initial work in Phase 2.

UAS↗
NASA NTRS2009

Aerobot Autonomy Architecture

An architecture for autonomous operation of an aerobot (i.e., a robotic blimp) to be used in scientific exploration of planets and moons in the Solar system with an atmosphere (such as Titan and Venus) is undergoing development. This architecture is also applicable to autonomous airships that could be flown in the terrestrial atmosphere for scientific exploration, military reconnaissance and surveillance, and as radio-communication relay stations in disaster areas. The architecture was conceived to satisfy requirements to perform the following functions: a) Vehicle safing, that is, ensuring the integrity of the aerobot during its entire mission, including during extended communication blackouts. b) Accurate and robust autonomous flight control during operation in diverse modes, including launch, deployment of scientific instruments, long traverses, hovering or station-keeping, and maneuvers for touch-and-go surface sampling. c) Mapping and self-localization in the absence of a global positioning system. d) Advanced recognition of hazards and targets in conjunction with tracking of, and visual servoing toward, targets, all to enable the aerobot to detect and avoid atmospheric and topographic hazards and to identify, home in on, and hover over predefined terrain features or other targets of scientific interest. The architecture is an integrated combination of systems for accurate and robust vehicle and flight trajectory control; estimation of the state of the aerobot; perception-based detection and avoidance of hazards; monitoring of the integrity and functionality ("health") of the aerobot; reflexive safing actions; multi-modal localization and mapping; autonomous planning and execution of scientific observations; and long-range planning and monitoring of the mission of the aerobot. The prototype JPL aerobot (see figure) has been tested extensively in various areas in the California Mojave desert.

Elfes, Alberto↗
NASA NTRS2015

NASA ACES V&V Alignment Briefing

Regulations to establish operational and performance requirements for unmanned aircraft systems (UAS) are being developed by a consortium of government, industry and academic institutions. Those requirements will apply to the new detect and avoid (DAA) systems and other equipment necessary to integrate UAS with the National Airspace System (NAS) and are determined according to their contribution to the overall safety case for such an integration. This briefing focuses on providing an overview of the Airspace Concept Evaluation System (ACES) platform, review of detect-and-avoid models incorprated in ACES, sumamry of two planned ACES studies, and a way forward to impact the SC-228 VV plan.

Santiago, Confesor↗
NASA NTRS1995

Flight test of a low-altitude helicopter guidance system with obstacle avoidance capability

Military aircraft regularly conduct missions that include low-atltitude, near-terrain flight in order to increase covertness and payload effectiveness. Civilian applications include airborne fire fighting, police surveillance, search and rescue, and helicopter emergency medical service. Several fixed-wing aircraft now employ terrain elevation maps and forward-pointed radars to achieve automated terrain following or terrain avoidance flight. Similar systems specialized to helicopters and their flight regime have not received as much attention. A helicopter guidance system relying on digitized terrain elevation maps has been developed that employs airborne navigation, mission requirements, aircraft performance limits, and radar altimeter returns to generate a valley-seeking, low-altitude trajectory between waypoints. The guidance trajectory is symbolically presented to the pilot on a helmet mounted display. This system has been flight tested to 150 ft (45.7 m) above ground level altitude at 80 kts, and is primarily limited by the ability of the pilot to perform manual detection and avoidance of unmapped hazards. In this study, a wide field of view laser radar sensor has been incorporated into this guidance system to assist the pilot in obstacle detection and avoidance, while expanding the system's operational flight envelope. The results from early flight tests of this system are presented. Low-altitude missions to 100 ft (30.5 m) altitude at 80n kts in the presence of unmapped natural and man-made obstacles were demonstrated while the pilot maintained situational awareness and tracking of the guidance trajectory. Further reductions in altitude are expected with continued flight testing.

Zelenka, Richard E.↗
NASA NTRS2017

Operational Overview for UAS Integration in the NAS Project Flight Test Series 3

The National Aeronautics and Space Administration Unmanned Aircraft Systems Integration in the National Airspace System Project has conducted a series of flight tests intended to support the reduction of barriers that prevent unmanned aircraft from flying without the required waivers from the Federal Aviation Administration. The 2015 Flight Test Series 3, supported two separate test configurations. The first configuration investigated the timing of Detect and Avoid alerting thresholds using a radar equipped unmanned vehicle and multiple live intruders flown at varying encounter geometries. The second configuration included a surrogate unmanned vehicle (flown from a ground control station, with a safety pilot on board) flying a mission in a virtual air traffic control airspace sector using research pilot displays and Detect and Avoid advisories to maintain separation from live and virtual aircraft. The test was conducted over an eight-week span within the R-2508 Special Use Airspace. Over 200 encounters were flown for the first configuration, and although the second configuration was cancelled after three data collection flights, Flight Test 3 proved to be invaluable for the purposes of planning, managing, and execution of this type of integrated flight test.

unmanned aircraft systems↗
NASA NTRS1993

Robotic Vision With Enhanced Detection Of Edges

Robotic vision subsystem provides enhanced detection of edges as it preprocesses image of target moving in six degrees of freedom. Subsystem designed to filter out high (spatial) frequency components in image, with frequency response tuned to size of object detected. Blurring and background noise reduced to avoid false detection of moving target. Image produced used by another vision subsystem guiding robot to mate with target. Produces less noise and operates more reliably.

Davis, V. L.↗
NASA NTRS2016

Formal Analysis of Extended Well-Clear Boundaries for Unmanned Aircraft

This paper concerns the application of formal methods to the definition of a detect and avoid concept for unmanned aircraft systems (UAS). In particular, it illustrates how formal analysis was used to explain and correct unexpected behaviors of the logic that issues alerts when two aircraft are predicted not to be well clear from one another. As a result of this analysis, a recommendation was proposed to, and subsequently adopted by, the US standards organization that defines the minimum operational requirements for the UAS detect and avoid concept.

Munoz, Cesar↗
NASA NTRS2016

Precision Landing and Hazard Avoidance Doman

The Precision Landing and Hazard Avoidance (PL&HA) domain addresses the development, integration, testing, and spaceflight infusion of sensing, processing, and GN&C functions critical to the success and safety of future human and robotic exploration missions. PL&HA sensors also have applications to other mission events, such as rendezvous and docking. Autonomous PL&HA builds upon the core GN&C capabilities developed to enable soft, controlled landings on the Moon, Mars, and other solar system bodies. Through the addition of a Terrain Relative Navigation (TRN) function, precision landing within tens of meters of a map-based target is possible. The addition of a 3-D terrain mapping lidar sensor improves the probability of a safe landing via autonomous, real-time Hazard Detection and Avoidance (HDA). PL&HA significantly improves the probability of mission success and enhances access to sites of scientific interest located in challenging terrain. PL&HA can also utilize external navigation aids, such as navigation satellites and surface beacons. Advanced Lidar Sensors High precision ranging, velocimetry, and 3-D terrain mapping Terrain Relative Navigation (TRN) TRN compares onboard reconnaissance data with real-time terrain imaging data to update the S/C position estimate Hazard Detection and Avoidance (HDA) Generates a high-resolution, 3-D terrain map in real-time during the approach trajectory to identify safe landing targets Inertial Navigation During Terminal Descent High precision surface relative sensors enable accurate inertial navigation during terminal descent and a tightly controlled touchdown within meters of the selected safe landing target.

Robertson, Edward A.↗
NASA NTRS2021

LiDAR-Inertial Based Navigation and Mapping for Precision Landing

Future lander missions will travel to ambitious, scientifically interesting locations near rough and dangerous terrain. They will need to operate with limited prior information about the terrain, and under varying lighting conditions. Landing safely and precisely in the face of these challenges is difficult for existing vision-based landing systems, which require detailed orbital reconnaissance, a priori hazard maps, and impose time-of-day restrictions on landing to ensure similar lighting conditions in orbital and descent imagery. Advanced 3D imaging LiDAR systems currently under development, and originally intended for single-scan hazard detection, have the potential to be operated continuously from altitudes of up to 5 km. Used together with existing inertial measurement units (IMUs), these sensors open a path-to-flight for a full navigation and mapping system, which could replace or augment a traditional landing sensor suite. A landing system based around these sensors can perform accurate altimetry, map-relative localization (MRL), LiDAR-inertial odometry, and map refinement in an illumination-insensitive manner, over unknown or partially known terrain. This paper outlines preliminary work on a LiDAR-inertial landing system that: estimates the spacecraft trajectory during entry, descent, and landing (EDL); and maps the topography of the terrain below, for future use in hazard detection and avoidance. An incremental, factor graph based, smoothing approach is used to solve for the maximum a posteriori trajectory of spacecraft states. Integrated IMU measurements and features tracked in adjacent range and intensity images are used to estimate motion (LiDAR-inertial odometry). LiDAR scans are binned into motion-corrected digital elevation models (DEMs), which are matched to an existing orbital topographic map to provide absolute position information (MRL). The estimated trajectory is then used to project the LiDAR scans into the map frame, creating a variable-resolution quadtree topographic map suitable for hazard detection and avoidance. Existing topographic maps from throughout the solar system (i.e., Earth, the Moon, Mars, Ceres, Vesta, Europa, Enceladus, and Eros) are upsampled for use in EDL simulations. The Mars 2020 Lander Vision System Simulator (LVSS) is extended to simulate LiDAR-inertial data for realistic EDL trajectories. Results of the algorithm operating on the simulated data are presented. Estimated spacecraft trajectory and refined map are compared to ground truth to assess estimation accuracy.

Katake, Anup↗
NASA NTRS2016

Flight Test Overview for UAS Integration in the NAS Project

The National Aeronautics and Space Administration is conducting a series of flight tests intended to support the reduction of barriers that prevent unmanned aircraft from flying without the required waivers from the Federal Aviation Administration. The most recent testing supported two separate test configurations. The first investigated the timing of Detect and Avoid (DAA) alerting thresholds using a radar-equipped unmanned vehicle and multiple live intruders flown at varying encounter geometries. The second configuration included a surrogate unmanned vehicle (flown from a ground control station, with a safety pilot on board) flying a mission in a virtual air traffic control airspace sector using research pilot displays and DAA advisories to maintain separation from live and virtual aircraft. The test was conducted over a seven-week span in the summer of 2015. The data from over 100 encounter sorties will be used to inform the RTCA Phase 1 Detect and Avoid and Command and Control Minimum Operating Performance Standards (MOPS) intended to be completed by the summer of 2016. Follow-on flight-testing is planned for the spring of 2016 to capture remaining encounters and support validation of the MOPS.

UAS↗