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At least 235 records · Page 13

Separation at Crossing Waypoints Under Wind Uncertainty in Urban Air Mobility

To enable high-density operations in major metropolitan areas, urban air mobility networks are anticipated to have air traffic management with higher levels of autonomy. To ensure that this type of autonomy is feasible, one of the critical steps from a safety and efficiency perspective is understanding various factors that affect the spatial separation between airborne flights and ensure that these factors can be managed. In terms of separation assurance and scheduling, an important real-world concern is that future states of aircraft cannot be perfectly predicted. The focus of this research paper is to understand how these prediction errors affect separation and scheduling services and to explore mitigation strategies to handle these errors. In this research, we have simulated these types of uncertainty by adding wind-prediction errors to trajectory predictions for separation. With these wind-prediction errors, we decompose the problem into two separate questions. First, using both simulation and analytical methods, we look at conflict-detection-only scenarios to understand how the wind errors affect required minimum temporal separation between crossing flights to ensure a specific spatial separation. Next, we study how trajectory errors effect conflict resolution, and we explore different combinations of scheduling and separation assurance to mitigate the effects of uncertainty between crossing flights. The conflict resolution algorithm aims to minimize necessary temporal separation between crossing flights under uncertainty, still ensuring safety-critical spatial separation. In summary, this research suggests that the required minimum temporal separation at a crossing waypoint is dependent on factors such as inbound crossing angle, the relative angle between wind direction and bearing of each route, wind magnitude, wind magnitude uncertainty, nominal cruise airspeed of aircraft, and look-ahead time of the conflict detection algorithm. This research also suggests that different combinations of scheduling and separation have different qualitative results. Using a combination of strategic, flow-based scheduling, tactical scheduling at crossings, speed control near crossing points, and separation management leads to a system that is insensitive to trajectory prediction errors with high throughput and flexibility for aircraft away from shared resources.

urban air mobility↗

Separation at Crossing Waypoints Under Wind Uncertainty in Urban Air Mobility

To enable high-density operations in major metropolitan areas, urban air mobility networks are anticipated to have air traffic management with higher levels of autonomy. To ensure that this type of autonomy is feasible, one of the critical steps from a safety and efficiency perspective is understanding various factors that affect the spatial separation between airborne flights and ensure that these factors can be managed. In terms of separation assurance and scheduling, an important real-world concern is that future states of aircraft cannot be perfectly predicted. The focus of this research paper is to understand how these prediction errors affect separation and scheduling services and to explore mitigation strategies to handle these errors. In this research, we have simulated these types of uncertainty by adding wind-prediction errors to trajectory predictions for separation. With these wind-prediction errors, we decompose the problem into two separate questions. First, using both simulation and analytical methods, we look at conflict-detection-only scenarios to understand how the wind errors affect required minimum temporal separation between crossing flights to ensure a specific spatial separation. Next, we study how trajectory errors effect conflict resolution, and we explore different combinations of scheduling and separation assurance to mitigate the effects of uncertainty between crossing flights. The conflict resolution algorithm aims to minimize necessary temporal separation between crossing flights under uncertainty, still ensuring safety-critical spatial separation. In summary, this research suggests that the required minimum temporal separation at a crossing waypoint is dependent on factors such as inbound crossing angle, the relative angle between wind direction and bearing of each route, wind magnitude, wind magnitude uncertainty, nominal cruise airspeed of aircraft, and look-ahead time of the conflict detection algorithm. This research also suggests that different combinations of scheduling and separation have different qualitative results. Using a combination of strategic, flow-based scheduling, tactical scheduling at crossings, speed control near crossing points, and separation management leads to a system that is insensitive to trajectory prediction errors with high throughput and flexibility for aircraft away from shared resources.

urban air mobility↗

NASA Armstrong Flight Research Center Dynamics and Controls (530)

AFRC Controls and Dynamics Branch - Research Areas: - Traditional GN&C - Classical and advanced control algorithms - Risk based approaches for safety critical applications - Integration of novel sensors and sensor fusion (FOSS, LIDAR, etc.) - Trajectory optimization and control - Flight/System Dynamics - Equations of Motion and integrated modeling of vehicles and vehicle systems - Unique and novel vehicle dynamics - Methods for extracting relevant vehicle dynamic information from flight data - Human and vehicle interfaces and interactions - Handling Qualities and Pilot-in-the-loop oscillations (PIO) predictions and design metrics - Ride quality research - Autonomy - Novel algorithms, sensors, and sensor fusion - Bounding risk for testing automated systems - Pilot/Operator interactions - Engineering Support and Airworthiness Assessments - Aircraft modifications and experimental configurations

Chris Miller↗

Human Automation Teaming: Lessons Learned and Future Directions

Full autonomy seems to be the goal for system developers in almost every area of the economy. However, as we move from automated systems to autonomous systems, designers have needed to insert humans to oversee automation that has traditionally been brittle or incomplete. This creates its own problems as the operator is usually out of the loop when the automation hands over problems that it cannot handle. To better handle these situations, it has been proposed that we develop human automation teams that have shared goals and objectives to support task performance. This paper will describe an initial model of Human Automation Teaming (HAT) which has three elements: transparency, bi-directional communications, and human-directed execution. Transparency in our model is a method for giving insight into the reasoning behind automated recommendations and actions, bi-directional communication allows the operator to communicate directly with the automation, and finally the automation defers execution to the human. The model was implemented through a number of features on an electronic flight bag (EFB) which are described in the paper. The EFB was installed in a mid-fidelity flight simulator and used by 12 airline pilots to support diversion decisions during off-nominal flight scenarios. Pilots reported that working with the HAT automation made diversion decisions easier and reduced their workload. They also reported that the information provided about diversion airports was similar to what they would receive from ground dispatch, thus making coordination with dispatch easier and less time consuming. These HAT features engender more trust in the automation when appropriate, and less when not, allowing improved supervision of automated functions by flight crews.

Human-Autonomy Teaming↗

NASA Electrified Aircraft Propulsion Efforts

NASA's broad investments in Electrified Aircraft Propulsion (EAP) are reviewed in this paper. NASA investments are guided by an assessment of potential market impacts, technical key performance parameters, and technology readiness attained through a combination of studies, enabling fundamental research, and flight research. NASA has determined that the impact of EAP varies by market and NASA is considering three markets: national/international, on-demand mobility, and short haul regional air transport. Flight research is underway to demonstrate integrated solutions and inform standards and certification processes. This paper focuses on the vehicle related activities, however there are related NASA activities in air space management and vehicle autonomy activities as well as a breakthrough technology project called the Convergent Aeronautics Solutions Project. A key finding is that sufficient technical advances in key areas have been made which indicate EAP is a viable technology for aircraft. Significant progress has been made to reduce EAP adoption barriers and further work is needed to transition the technology to a commercial product and improve the technology so it is applicable to large transonic aircraft. This paper will review the activities of the Hybrid Gas Electric Subproject of the Advanced Air Transport Technology Project, the Revolutionary Vertical Lift Technology Project, and the X-57 Flight Demonstration Project, and discuss the potential EAP benefits for commercial and military applications.

Jansen, Ralph H.↗

A Distributed Simulation-to-Flight Framework to Support Investigating Trust/Trustworthiness in Multi-Agent Systems

As autonomous systems continue to grow both in use and complexity, the necessity for robust and extensible simulation-to-flight frameworks is paramount for establishing an effective architecture for autonomous systems. Hardware test flights are time-consuming and cost prohibitive during early system design and development. Simulation environments can be useful tools to accelerate algorithm development and testing. However, transitions from simulation to flight (sim-to-flight) can be challenging, unless systems are designed with this transition in mind and with the necessary capabilities built into the architecture and framework. One of the objectives of Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR) was to design and develop a distributed mixed-reality simulation environment to begin establishing a basis for certification of autonomous systems via research into trust and trustworthiness. ATTRACTOR’s objective was to construct computational concepts of trustworthiness and justifiable trust in multi-agent autonomous teams, to inform future certification of safety-critical and time-critical autonomous systems in aviation. In this paper, we present an autonomous systems architecture and development framework paired with a persistent distributed modeling and simulation (ModSim) environment for test and evaluation of autonomous systems. They were designed under ATTRACTOR in order to measure and establish trustworthiness and trust in single-and multi-agent human-machine systems whether these machines are fixed-wing general aviation, rotary-wing Unmanned Aerial Vehicles (UAVs), ground rovers, or even spacecraft. The Autonomous Entity Operational Network (AEON) framework enables autonomous system development with an easily extensible collection of libraries and plug-n-play nodes facilitated by the Data Distribution Service (DDS) communication protocol standard. The Baseline Environment for Autonomous Modeling (BEAM) simulation environment is a distributed mixed-reality Unity™-based environment built around the same DDS communication paradigm allowing for easy integration with AEON-based autonomous applications, enabling sim-to-flight with minimal configuration changes. Using AEON and BEAM, source code that runs in simulation ports directly to hardware and has successfully flown in the National Airspace System (NAS) at NASA LaRC many times over the lifetime of ATTRACTOR.

Benjamin N Kelley↗

Concepts for Distributed Sensing and Collaborative Airspace Autonomy in Advanced Urban Air Mobility

Emerging concepts for advanced urban air mobility envision responsive air transportation capabilities that will safely move people and cargo in locations presently underserved by aviation. Expanding aviation services to these locales, particularly for high-density autonomous flight operations over urban centers, will require advances beyond the state-of-the-art techniques for airborne sensing. The emerging field of distributed sensing and ‘smart spaces’ – where sensing, processing, communication, and actuation are embedded in the environment in which agents are acting – may provide attractive alternatives over traditional aviation solutions. This paper outlines the challenges and opportunities for distributed sensing and smart space concepts to meet the emerging needs of advanced urban operations in the national airspace. We present an overview of distributed sensing concepts and research currently being investigated under this endeavor.

Distributed sensing↗

Model Based Autonomy for Robust Mars Operations

Space missions have historically relied upon a large ground staff, numbering in the hundreds for complex missions, to maintain routine operations. When an anomaly occurs, this small army of engineers attempts to identify and work around the problem. A piloted Mars mission, with its multiyear duration, cost pressures, half-hour communication delays and two-week blackouts cannot be closely controlled by a battalion of engineers on Earth. Flight crew involvement in routine system operations must also be minimized to maximize science return. It also may be unrealistic to require the crew have the expertise in each mission subsystem needed to diagnose a system failure and effect a timely repair, as engineers did for Apollo 13. Enter model-based autonomy, which allows complex systems to autonomously maintain operation despite failures or anomalous conditions, contributing to safe, robust, and minimally supervised operation of spacecraft, life support, In Situ Resource Utilization (ISRU) and power systems. Autonomous reasoning is central to the approach. A reasoning algorithm uses a logical or mathematical model of a system to infer how to operate the system, diagnose failures and generate appropriate behavior to repair or reconfigure the system in response. The 'plug and play' nature of the models enables low cost development of autonomy for multiple platforms. Declarative, reusable models capture relevant aspects of the behavior of simple devices (e.g. valves or thrusters). Reasoning algorithms combine device models to create a model of the system-wide interactions and behavior of a complex, unique artifact such as a spacecraft. Rather than requiring engineers to all possible interactions and failures at design time or perform analysis during the mission, the reasoning engine generates the appropriate response to the current situation, taking into account its system-wide knowledge, the current state, and even sensor failures or unexpected behavior.

Kurien, James A.↗

VML 3.0 Reactive Sequencing Objects and Matrix Math Operations for Attitude Profiling

VML (Virtual Machine Language) has been used as the sequencing flight software on over a dozen JPL deep-space missions, most recently flying on GRAIL and JUNO. In conjunction with the NASA SBIR entitled "Reactive Rendezvous and Docking Sequencer", VML version 3.0 has been enhanced to include object-oriented element organization, built-in queuing operations, and sophisticated matrix / vector operations. These improvements allow VML scripts to easily perform much of the work that formerly would have required a great deal of expensive flight software development to realize. Autonomous turning and tracking makes considerable use of new VML features. Profiles generated by flight software are managed using object-oriented VML data constructs executed in discrete time by the VML flight software. VML vector and matrix operations provide the ability to calculate and supply quaternions to the attitude controller flight software which produces torque requests. Using VML-based attitude planning components eliminates flight software development effort, and reduces corresponding costs. In addition, the direct management of the quaternions allows turning and tracking to be tied in with sophisticated high-level VML state machines. These state machines provide autonomous management of spacecraft operations during critical tasks like a hypothetic Mars sample return rendezvous and docking. State machines created for autonomous science observations can also use this sort of attitude planning system, allowing heightened autonomy levels to reduce operations costs. VML state machines cannot be considered merely sequences - they are reactive logic constructs capable of autonomous decision making within a well-defined domain. The state machine approach enabled by VML 3.0 is progressing toward flight capability with a wide array of applicable mission activities.

VML (Virtual Machine Language)↗

Role of automation in the ACRV operations

The Assured Crew Return Vehicle (ACRV) will provide the Space Station Freedom with contingency means of return to earth (1) of one disabled crew member during medical emergencies, (2) of all crew members in case of accidents or failures of SSF systems, and (3) in case of interruption of the Space Shuttle flights. A wide range of vehicle configurations and system approaches are currently under study. The Program requirements focus on minimizing life cycle costs by ensuring simple operations, built-in reliability and maintainability. The ACRV philosophy of embedded operations is based on maximum use of existing facilities, resources and processes, while minimizing the interfaces and impacts to the Space Shuttle and Freedom programs. A preliminary integrated operations concept based on this philosophy and covering the ground, flight, mission support, and landing and recovery operations has been produced. To implement the ACRV operations concept, the underlying approach has been to rely on vehicle autonomy and automation, to the extent possible. Candidate functions and processes which may benefit from current or near-term automation and robotics technologies are identified. These include, but are not limited to, built-in automated ground tests and checkouts; use of the Freedom and the Orbiter remote manipulator systems, for ACRV berthing; automated passive monitoring and performance trend analysis, and periodic active checkouts during dormant periods. The major ACRV operations concept issues as they relate to the use of automation are discussed.

Sepahban, S. F.↗

NASA IVHM Technology Experiment for X-vehicles (NITEX)

The purpose of the NASA IVHM Technology Experiment for X-vehicles (NITEX) is to advance the development of selected IVHM technologies in a flight environment and to demonstrate the potential for reusable launch vehicle ground processing savings. The technologies to be developed and demonstrated include system-level and detailed diagnostics for real-time fault detection and isolation, prognostics for fault prediction, automated maintenance planning based on diagnostic and prognostic results, and a microelectronics hardware platform. Complete flight The Evolution of Flexible Insulation as IVHM consists of advanced sensors, distributed data acquisition, data processing that includes model-based diagnostics, prognostics and vehicle autonomy for control or suggested action, and advanced data storage. Complete ground IVHM consists of evolved control room architectures, advanced applications including automated maintenance planning and automated ground support equipment. This experiment will advance the development of a subset of complete IVHM.

Sandra, Hayden↗

Haptic-Multimodal Flight Control System Update

The rapidly advancing capabilities of autonomous aircraft suggest a future where many of the responsibilities of today s pilot transition to the vehicle, transforming the pilot s job into something akin to driving a car or simply being a passenger. Notionally, this transition will reduce the specialized skills, training, and attention required of the human user while improving safety and performance. However, our experience with highly automated aircraft highlights many challenges to this transition including: lack of automation resilience; adverse human-automation interaction under stress; and the difficulty of developing certification standards and methods of compliance for complex systems performing critical functions traditionally performed by the pilot (e.g., sense and avoid vs. see and avoid). Recognizing these opportunities and realities, researchers at NASA Langley are developing a haptic-multimodal flight control (HFC) system concept that can serve as a bridge between today s state of the art aircraft that are highly automated but have little autonomy and can only be operated safely by highly trained experts (i.e., pilots) to a future in which non-experts (e.g., drivers) can safely and reliably use autonomous aircraft to perform a variety of missions. This paper reviews the motivation and theoretical basis of the HFC system, describes its current state of development, and presents results from two pilot-in-the-loop simulation studies. These preliminary studies suggest the HFC reshapes human-automation interaction in a way well-suited to revolutionary ease-of-use.

Goodrich, Kenneth H.↗

Reinforcement Learning with Autonomous Small Unmanned Aerial Vehicles in Cluttered Environments

We present ongoing work in the Autonomy Incubator at NASA Langley Research Center (LaRC) exploring the efficacy of a data set aggregation approach to reinforcement learning for small unmanned aerial vehicle (sUAV) flight in dense and cluttered environments with reactive obstacle avoidance. The goal is to learn an autonomous flight model using training experiences from a human piloting a sUAV around static obstacles. The training approach uses video data from a forward-facing camera that records the human pilot's flight. Various computer vision based features are extracted from the video relating to edge and gradient information. The recorded human-controlled inputs are used to train an autonomous control model that correlates the extracted feature vector to a yaw command. As part of the reinforcement learning approach, the autonomous control model is iteratively updated with feedback from a human agent who corrects undesired model output. This data driven approach to autonomous obstacle avoidance is explored for simulated forest environments furthering autonomous flight under the tree canopy research. This enables flight in previously inaccessible environments which are of interest to NASA researchers in Earth and Atmospheric sciences.

Tran, Loc↗

Spacecraft autonomy using onboard processing for a SAR constellation mission

The Autonomous Sciencecraft Experiment (ASE) will fly onboard the Air Force TechSat 21 constellation of three spacecraft scheduled for launch in 2006. ASE uses onboard continuous planning, robust task and goal-based execution, model-based mode identification and reconfiguration, and onboard machine learning and pattem recognition to radically increase science retum by enabling intelligent downlink selection and autonomous retargeting. Demonstration of these capabilities in a flight environment will open up tremendous new opportunities in planetary science, space physics, and earth science that would be unreachable without this technology.

Autonomous Sciencecraft Experiment ASE spacecraft ↗

Testing Planetary Rovers: Technologies, Perspectives, and Lessons Learned

Rovers are a vital component of NASA's strategy for manned and unmanned exploration of space. For the past five years, the Intelligent Mechanisms Group at the NASA Ames Research Center has conducted a vigorous program of field testing of rovers from both technology and science team productivity perspective. In this talk, I will give an overview of the the last two years of the test program, focusing on tests conducted in the Painted Desert of Arizona, the Atacama desert in Chile, and on IMG participation in the Mars Pathfinder mission. An overview of autonomy, manipulation, and user interface technologies developed in response to these missions will be presented, and lesson's learned in these missions and their impact on future flight missions will be presented. I will close with some perspectives on how the testing program has affected current rover systems.

Thomas, Hans↗

Hazard Perception & Avoidance (HPA): Part Task 1 - Results Outbrief

n April 2022, the Human Autonomy Teaming Lab (NASA Ames Research Center) conducted a manned, human-in-the-loop (HITL) simulation. This part task HITL began the lab’s Hazard Perception and Avoidance (HPA) technical work under NASA’s Advanced Air Mobility (AAM), Automated Flight and Contingency Management (AFCM) Sub-Project. The goals were to assess levels of automation for manned, electric vertical takeoff and landing (eVTOL) aircraft. This simulation tested manual and automated Resolution Advisory (RA) responses and return-to-course (RTC) maneuvers for the first version of the Airborne Collision Avoidance System’s (ACAS) rotary-wing (Xr) variant. This was conducted on a fixed-based simulator designed to fly eVTOL aircraft while maneuvering for intruding traffic. Variables for this study included levels of autonomy (i.e., manual and automated) as well as the types of alerts at the onset of conflicts (i.e., Corrective and RA). The data collected included response times, losses of well clear, and maneuver sizes and durations as well as subjective ratings like acceptability, and perceived workload. Additional details and future anticipations are also discussed.

air taxis↗

Concept, Design, & Implementation of a Remote Vehicle Operations Center for Autonomous Missions

The National Aeronautics and Space Administration is supporting research to develop a prototype remote vehicle operations center at Langley Research Center to explore current and future advanced air mobility operations using small unmanned aerial systems vehicles as surrogates for future, larger-scale passenger carrying vehicles. The prototype facility known as the Remote Operations for Autonomous Missions (ROAM) Unmanned Aerial Systems (UAS) Operations Center is being used to explore different roles and responsibilities of remote operators managing multiple autonomous vehicles, with the goal of exploring human-autonomy teaming concepts that enable m:N operations (i.e., m operators managing N vehicles). ROAM has developed into a world-class research, development, and technology (RD&T) environment that can support both the collection of human factors data and the command and control of remote vehicles in beyond visual line of sight conditions. ROAM provides a key capability to enable full end-to-end hardware- and human-in-the-loop simulation testing, connecting with simulated small-UAS and creating a seamless Live-Virtual-Constructive (LVC) environment. This report describes the development of the ROAM UAS Operations Center from concept through design, culminating in the current implementation at NASA’s Langley Research Center.

CERTAIN↗

Aerial Vehicle Surveys of other Planetary Atmospheres and Surfaces: Imaging, Remote-sensing, and Autonomy Technology Requirements

The objective of this paper is to review the anticipated imaging and remote-sensing technology requirements for aerial vehicle survey missions to other planetary bodies in our Solar system that can support in-atmosphere flight. In the not too distant future such planetary aerial vehicle (a.k.a. aerial explorers) exploration missions will become feasible. Imaging and remote-sensing observations will be a key objective for these missions. Accordingly, it is imperative that optimal solutions in terms of imaging acquisition and real-time autonomous analysis of image data sets be developed for such vehicles.

Young, Larry A.↗