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NASA NTRS2017

Dynamic Routing of Aircraft in the Presence of Adverse Weather Using a POMDP Framework

Each year weather-related airline delays result in hundreds of millions of dollars in additional fuel burn, maintenance, and lost revenue, not to mention passenger inconvenience. The current approaches for aircraft route planning in the presence of adverse weather still mainly rely on deterministic methods. In contrast, this work aims to deal with the problem using a Partially Observable Markov Decision Processes (POMDPs) framework, which allows for reasoning over uncertainty (including uncertainty in weather evolution over time) and results in solutions that are more robust to disruptions. The POMDP-based decision support system is demonstrated on several scenarios involving convective weather cells and is benchmarked against a deterministic planning system with functionality similar to those currently in use or under development.

Decision making↗

NASA NTRS2000

FACET: Future ATM Concepts Evaluation Tool

FACET (Future ATM Concepts Evaluation Tool) is an Air Traffic Management research tool being developed at the NASA Ames Research Center. This paper describes the design, architecture and functionalities of FACET. The purpose of FACET is to provide E simulation environment for exploration, development and evaluation of advanced ATM concepts. Examples of these concepts include new ATM paradigms such as Distributed Air-Ground Traffic Management, airspace redesign and new Decision Support Tools (DSTs) for controllers working within the operational procedures of the existing air traffic control system. FACET is currently capable of modeling system-wide en route airspace operations over the contiguous United States. Airspace models (e.g., Center/sector boundaries, airways, locations of navigation aids and airports) are available from databases. A core capability of FACET is the modeling of aircraft trajectories. Using round-earth kinematic equations, aircraft can be flown along flight plan routes or great circle routes as they climb, cruise and descend according to their individual aircraft-type performance models. Performance parameters (e.g., climb/descent rates and speeds, cruise speeds) are obtained from data table lookups. Heading, airspeed and altitude-rate dynamics are also modeled. Additional functionalities will be added as necessary for specific applications. FACET software is written in Java and C programming languages. It is platform-independent, and can be run on a variety of computers. FACET has been designed with a modular software architecture to enable rapid integration of research prototype implementations of new ATM concepts. There are several advanced ATM concepts that are currently being implemented in FACET airborne separation assurance, dynamic density predictions, airspace redesign (re-sectorization), benefits of a controller DST for direct-routing, and the integration of commercial space transportation system operations into the U.S. National Airspace System (NAS).

Bilmoria, Karl D.↗

NASA NTRS2012

Traverse Planning Experiments for Future Planetary Surface Exploration

The purpose of the investigation is to evaluate methodology and data requirements for remotely-assisted robotic traverse of extraterrestrial planetary surface to support human exploration program, assess opportunities for in-transit science operations, and validate landing site survey and selection techniques during planetary surface exploration mission analog demonstration at Haughton Crater on Devon Island, Nunavut, Canada. Additionally, 1) identify quality of remote observation data sets (i.e., surface imagery from orbit) required for effective pre-traverse route planning and determine if surface level data (i.e., onboard robotic imagery or other sensor data) is required for a successful traverse, and if additional surface level data can improve traverse efficiency or probability of success (TRPF Experiment). 2) Evaluate feasibility and techniques for conducting opportunistic science investigations during this type of traverse. (OSP Experiment). 3) Assess utility of remotely-assisted robotic vehicle for landing site validation survey. (LSV Experiment).

Hoffman, Stephen J.↗

NASA NTRS2017

Considerations for an Integrated UAS CNS Architecture

The National Aeronautics and Space Administration (NASA) Glenn Research Center (GRC) is investigating revolutionary and advanced universal, reliable, always available, cyber secure and affordable Communication, Navigation, Surveillance (CNS) options for all altitudes of UAS operations. In Spring 2015, NASA issued a Call for Proposals under NASA Research Announcements (NRA) NNH15ZEA001N, Amendment 7 Subtopic 2.4. Boeing was selected to conduct a study with the objective to determine the most promising candidate technologies for Unmanned Air Systems (UAS) air-to-air and air-to-ground data exchange and analyze their suitability in a post-NextGen NAS environment. The overall objectives are to develop UAS CNS requirements and then develop architectures that satisfy the requirements for UAS in both controlled and uncontrolled air space. This contract is funded under NASAs Aeronautics Research Mission Directorates (ARMD) Aviation Operations and Safety Program (AOSP) Safe Autonomous Systems Operations (SASO) project and proposes technologies for the Unmanned Air Systems Traffic Management (UTM) service.There is a need for accommodating large-scale populations of Unmanned Air Systems (UAS) in the national air space. Scale obviously impacts capacity planning for Communication, Navitation, and Surveillance (CNS) technologies. For example, can wireless communications data links provide the necessary capacity for accommodating millions of small UASs (sUAS) nationwide? Does the communications network provide sufficient Internet Protocol (IP) address space to allow air traffic control to securely address both UAS teams as a whole as well as individual UAS within each team? Can navigation and surveillance approaches assure safe route planning and safe separation of vehicles even in crowded skies?Our objective is to identify revolutionary and advanced CNS alternatives supporting UASs operating at all altitudes and in all airspace while accurately navigating in the absence of navigational aids. These CNS alternatives must be reliable, redundant, always available, cyber-secure, and affordable for all types of vehicles including small UAS to large transport category aircraft. The approach will identify CNS technology candidates that can meet the needs of the range of UAS missions to specific air traffic management applications where they will be most beneficial and cost effective.

Templin, Fred L.↗

NASA NTRS2017

Considerations for an Integrated UAS CNS Architecture

The National Aeronautics and Space Administration (NASA) Glenn Research Center (GRC) is investigating revolutionary and advanced universal, reliable, always available, cyber secure and affordable Communication, Navigation, Surveillance (CNS) options for all altitudes of UAS operations. In Spring 2015, NASA issued a Call for Proposals under NASA Research Announcements (NRA) NNH15ZEA001N, Amendment 7 Subtopic 2.4. Boeing was selected to conduct a study with the objective to determine the most promising candidate technologies for Unmanned Air Systems (UAS) air-to-air and air-to-ground data exchange and analyze their suitability in a post-NextGen NAS environment. The overall objectives are to develop UAS CNS requirements and then develop architectures that satisfy the requirements for UAS in both controlled and uncontrolled air space. This contract is funded under NASAs Aeronautics Research Mission Directorates (ARMD) Aviation Operations and Safety Program (AOSP) Safe Autonomous Systems Operations (SASO) project and proposes technologies for the Unmanned Air Systems Traffic Management (UTM) service.There is a need for accommodating large-scale populations of Unmanned Air Systems (UAS) in the national air space. Scale obviously impacts capacity planning for Communication, Navigation, and Surveillance (CNS) technologies. For example, can wireless communications data links provide the necessary capacity for accommodating millions of small UASs (sUAS) nationwide? Does the communications network provide sufficient Internet Protocol (IP) address space to allow air traffic control to securely address both UAS teams as a whole as well as individual UAS within each team? Can navigation and surveillance approaches assure safe route planning and safe separation of vehicles even in crowded skies?Our objective is to identify revolutionary and advanced CNS alternatives supporting UASs operating at all altitudes and in all airspace while accurately navigating in the absence of navigational aids. These CNS alternatives must be reliable, redundant, always available, cyber-secure, and affordable for all types of vehicles including small UAS to large transport category aircraft. The approach will identify CNS technology candidates that can meet the needs of the range of UAS missions to specific air traffic management applications where they will be most beneficial and cost effective.

Templin, Fred L.↗

NASA NTRS1988

Search Problems in Mission Planning and Navigation of Autonomous Aircraft

An architecture for the control of an autonomous aircraft is presented. The architecture is a hierarchical system representing an anthropomorphic breakdown of the control problem into planner, navigator, and pilot systems. The planner system determines high level global plans from overall mission objectives. This abstract mission planning is investigated by focusing on the Traveling Salesman Problem with variations on local and global constraints. Tree search techniques are applied including the breadth first, depth first, and best first algorithms. The minimum-column and row entries for the Traveling Salesman Problem cost matrix provides a powerful heuristic to guide these search techniques. Mission planning subgoals are directed from the planner to the navigator for planning routes in mountainous terrain with threats. Terrain/threat information is abstracted into a graph of possible paths for which graph searches are performed. It is shown that paths can be well represented by a search graph based on the Voronoi diagram of points representing the vertices of mountain boundaries. A comparison of Dijkstra's dynamic programming algorithm and the A* graph search algorithm from artificial intelligence/operations research is performed for several navigation path planning examples. These examples illustrate paths that minimize a combination of distance and exposure to threats. Finally, the pilot system synthesizes the flight trajectory by creating the control commands to fly the aircraft.

Krozel, James A.↗

NASA NTRSDate not supplied

Natural Language Processing Analysis of Notices to Airmen for Air Traffic Management Optimization

With new emerging technologies in the field of NLP, we explore their applications to digitize and analyze heritage Air Traffic Management (ATM) documents for planning and optimizing airspace operations. Specifically, this research focuses on harvesting semi-structured or un-structured information contained in Notices to Airmen (NOTAMs). Using NLP and other advanced data analytics, we will construct a data-driven framework which facilitates finding language patterns and the use of pretrained language models for classification and extraction of useful airspace constraints and restrictions. These may lead to tools that assist airspace users in understanding the constraints more efficiently, contributing to better route planning and safer execution. This paper explores three workflows entailing different NLP tasks. First, unsupervised techniques like word embedding and topic modeling are used for pattern finding and document classification. Second, a dataset is created by extracting information from the semi-structured NOTAM format as metadata for categorizing, visualizing, and extracting key entities driving NOTAM content. Third, modern pre-built deep learning based transformer models such as BERT, RoBERTa, and XLNet are evaluated on the question answering task, an even more robust approach to information extraction, as well as their respective fine-tuning tasks. In this work we include various performance metrics for the trained models to evaluate both accuracy and precision and we show that the models can be generalized for their respective tasks. The research work developed shows promise in uncovering trends in digital NOTAMs in the NAS and also offers a new framework for digitizing and inferring insights from free-form legacy NOTAMs, that are yet to be digitized.

Natural Language Processing↗

NASA NTRSDate not supplied

Natural Language Processing (NLP) Analysis of NOTAMs for Air Traffic Management Optimization

Natural Language Processing↗

NASA NTRS2008

The Role of Trust and Interaction in Global Positioning System Related Accidents

The Global Positioning System (GPS) uses a network of satellites to calculate the position of a receiver over time. This technology has revolutionized a wide range of safety-critical industries and leisure applications. These systems provide diverse benefits; supplementing the users existing navigation skills and reducing the uncertainty that often characterizes many route planning tasks. GPS applications can also help to reduce workload by automating tasks that would otherwise require finite cognitive and perceptual resources. However, the operation of these systems has been identified as a contributory factor in a range of recent accidents. Users often come to rely on GPS applications and, therefore, fail to notice when they develop faults or when errors occur in the other systems that use the data from these systems. Further accidents can stem from the over confidence that arises when users assume automated warnings will be issued when they stray from an intended route. Unless greater attention is paid to the role of trust and interaction in GPS applications then there is a danger that we will see an increasing number of these failures as positioning technologies become integral in the functioning of increasing numbers of applications.

Johnson, Chris W.↗

NASA NTRS2022

First 210 solar days of Mars 2020 Perseverance Robotic Operations – Mobility, Robotic Arm, Sampling, and Helicopter

This paper includes the summary, lessonslearned, and upcoming plans for the first 210 Mars solar days(sols) of the mission. The focus of the paper is on roboticoperations which has the primary responsibility for strategicplanning, uplink commanding and downlink analysis forrover mobility and navigation, robotic arm operation, thesampling and caching capability including coring, theadaptive caching assembly and the 2nd sample handlingrobotic arm, and interface to the Mars helicopter Ingenuity.As of Sol 210 the rover has driven 2663.65 meters, executed20764 robotic arm and sampling commands, and hassuccessfully completed 13 helicopter flights covering 2382meters horizontal distance. It includes the OperationsReadiness Tests in preparation for landing, landing and initialcheckouts, strategic route planning to the science destinationand waypoints, surface checkout of all of the roboticscapability of the rover. It also discusses the strategic planningand tactical agility needed for interleaving scienceinvestigation and technology demonstration of the Marshelicopter flights where a minimum distance had to bemaintained between the rover and helicopter during flights. Itdiscusses the challenges with planning robotic operations andaddressing anomalies with the larger uncertainty presentduring early mission operations. It also discusses the impacton robotic operations from lessons incorporated fromprevious missions.

Ono, Hiro↗

NASA NTRS2012

HURON (HUman and Robotic Optimization Network) Multi-Agent Temporal Activity Planner/Scheduler

HURON solves the problem of how to optimize a plan and schedule for assigning multiple agents to a temporal sequence of actions (e.g., science tasks). Developed as a generic planning and scheduling tool, HURON has been used to optimize space mission surface operations. The tool has also been used to analyze lunar architectures for a variety of surface operational scenarios in order to maximize return on investment and productivity. These scenarios include numerous science activities performed by a diverse set of agents: humans, teleoperated rovers, and autonomous rovers. Once given a set of agents, activities, resources, resource constraints, temporal constraints, and de pendencies, HURON computes an optimal schedule that meets a specified goal (e.g., maximum productivity or minimum time), subject to the constraints. HURON performs planning and scheduling optimization as a graph search in state-space with forward progression. Each node in the graph contains a state instance. Starting with the initial node, a graph is automatically constructed with new successive nodes of each new state to explore. The optimization uses a set of pre-conditions and post-conditions to create the children states. The Python language was adopted to not only enable more agile development, but to also allow the domain experts to easily define their optimization models. A graphical user interface was also developed to facilitate real-time search information feedback and interaction by the operator in the search optimization process. The HURON package has many potential uses in the fields of Operations Research and Management Science where this technology applies to many commercial domains requiring optimization to reduce costs. For example, optimizing a fleet of transportation truck routes, aircraft flight scheduling, and other route-planning scenarios involving multiple agent task optimization would all benefit by using HURON.

Hua, Hook↗

NASA NTRS1973

Remote sensing of sea ice motion

Remote sensing by ERTS-1 provides overlapping coverage on sequential days of dynamic changes in Arctic Sea ice and allows for route planning of shipping in the polar region.

Rango, A.↗

NASA NTRS1986

Programable Pipelined-Image Processor

Computer serves as pipelined processor for imagery or other two-dimensional digital data. Processor does feature extraction, smoothing, edge detection, texture measurement, and stereoscoptic area correlation. Also plans routes for obstacle avoidance by robots and solves two-dimensional partial differential equations. Image processor consists of modular units: each includes set of computing elements of types particularly useful in pipelined-image processing. Flexible interconnection scheme used to route data to subsequent stages of pipeline.

Gennery, D. B.↗

NASA NTRS1985

PIFEX: An advanced programmable pipelined-image processor

PIFEX is a pipelined-image processor being built in the JPL Robotics Lab. It will operate on digitized raster-scanned images (at 60 frames per second for images up to about 300 by 400 and at lesser rates for larger images), performing a variety of operations simultaneously under program control. It thus is a powerful, flexible tool for image processing and low-level computer vision. It also has applications in other two-dimensional problems such as route planning for obstacle avoidance and the numerical solution of two-dimensional partial differential equations (although its low numerical precision limits its use in the latter field). The concept and design of PIFEX are described herein, and some examples of its use are given.

Gennery, D. B.↗

NASA NTRS1986

A knowledge-based expert system for scheduling of airborne astronomical observations

KAOS (Kuiper Airborne Observatory Scheduler) is a knowledge-based expert system developed at NASA Ames Research Center to assist in route planning of a C-141 flying astronomical observatory. This program determines a sequence of flight legs that enables sequential observations of a set of heavenly bodies derived from a list of desirable objects. The possible flight legs are constrained by problems of observability, avoiding flyovers of warning and restricted military zones, and running out of fuel. A significant contribution of the KAOS program is that it couples computational capability with a reasoning system.

Nachtsheim, P. R.↗

NASA NTRS1985

A knowledge-based expert system for scheduling of airborne astronomical observations

The Kuiper Airborne Observatory Scheduler (KAOS) is a knowledge-based expert system developed at NASA Ames Research Center to assist in route planning of a C-141 flying astronomical observatory. This program determines a sequence of flight legs that enables sequential observations of a set of heavenly bodies derived from a list of desirable objects. The possible flight legs are constrained by problems of observability, avoiding flyovers of warning and restricted military zones, and running out of fuel. A significant contribution of the KAOS program is that it couples computational capability with a reasoning system.

Nachtsheim, P. R.↗

NASA NTRS1990

Processor Would Find Best Paths On Map

Proposed very-large-scale integrated (VLSI) circuit image-data processor finds path of least cost from specified origin to any destination on map. Cost of traversal assigned to each picture element of map. Path of least cost from originating picture element to every other picture element computed as path that preserves as much as possible of signal transmitted by originating picture element. Dedicated microprocessor at each picture element stores cost of traversal and performs its share of computations of paths of least cost. Least-cost-path problem occurs in research, military maneuvers, and in planning routes of vehicles.

Eberhardt, Silvio P.↗

NASA NTRS1991

The use of visual cues for vehicle control and navigation

At least three levels of control are required to operate most vehicles: (1) inner-loop control to counteract the momentary effects of disturbances on vehicle position; (2) intermittent maneuvers to avoid obstacles, and (3) outer-loop control to maintain a planned route. Operators monitor dynamic optical relationships in their immediate surroundings to estimate momentary changes in forward, lateral, and vertical position, rates of change in speed and direction of motion, and distance from obstacles. The process of searching the external scene to find landmarks (for navigation) is intermittent and deliberate, while monitoring and responding to subtle changes in the visual scene (for vehicle control) is relatively continuous and 'automatic'. However, since operators may perform both tasks simultaneously, the dynamic optical cues available for a vehicle control task may be determined by the operator's direction of gaze for wayfinding. An attempt to relate the visual processes involved in vehicle control and wayfinding is presented. The frames of reference and information used by different operators (e.g., automobile drivers, airline pilots, and helicopter pilots) are reviewed with particular emphasis on the special problems encountered by helicopter pilots flying nap of the earth (NOE). The goal of this overview is to describe the context within which different vehicle control tasks are performed and to suggest ways in which the use of visual cues for geographical orientation might influence visually guided control activities.

Hart, Sandra G.↗