A Learning-Based Guidance Selection Mechanism for a Formally Verified Sense and Avoid Algorithm

This paper describes a learning-based strategy for selecting conflict avoidance maneuvers for autonomous unmanned aircraft systems. The selected maneuvers are provided by a formally verified algorithm and they are guaranteed to solve any impending conflict under general assumptions about aircraft dynamics. The decision-making logic that selects the appropriate maneuvers is encoded in a stochastic policy encapsulated as a neural network. The networks parameters are optimized to maximize a reward function. The reward function penalizes loss of separation with other aircraft while rewarding resolutions that result in minimum excursions from the nominal flight plan. This paper provides a description of the technique and presents preliminary simulation results

Baseline Assessment and Prioritization Framework for IVHM Integrity Assurance Enabling Capabilities

Fundamental to vehicle health management is the deployment of systems incorporating advanced technologies for predicting and detecting anomalous conditions in highly complex and integrated environments. Integrated structural integrity health monitoring, statistical algorithms for detection, estimation, prediction, and fusion, and diagnosis supporting adaptive control are examples of advanced technologies that present considerable verification and validation challenges. These systems necessitate interactions between physical and software-based systems that are highly networked with sensing and actuation subsystems, and incorporate technologies that are, in many respects, different from those employed in civil aviation today. A formidable barrier to deploying these advanced technologies in civil aviation is the lack of enabling verification and validation tools, methods, and technologies. The development of new verification and validation capabilities will not only enable the fielding of advanced vehicle health management systems, but will also provide new assurance capabilities for verification and validation of current generation aviation software which has been implicated in anomalous in-flight behavior. This paper describes the research focused on enabling capabilities for verification and validation underway within NASA s Integrated Vehicle Health Management project, discusses the state of the art of these capabilities, and includes a framework for prioritizing activities

Model Checking at Scale: Automated Air Traffic Control Design Space Exploration

Many possible solutions, differing in the assumptions and implementations of the components in use, are usually in competition during early design stages. Deciding which solution to adopt requires considering several trade-offs. Model checking represents a possible way of comparing such designs, however, when the number of designs is large, building and validating so many models may be intractable. During our collaboration with NASA, we faced the challenge of considering a design space with more than 20,000 designs for the NextGen air traffic control system. To deal with this problem, we introduce a compositional, modular, parameterized approach combining model checking with contract-based design to automatically generate large numbers of models from a possible set of components and their implementations. Our approach is fully automated, enabling the generation and validation of all target designs. The 1,620 designs that were most relevant to NASA were analyzed exhaustively. To deal with the massive amount of data generated, we apply novel data-analysis techniques that enable a rich comparison of the designs, including safety aspects. Our results were validated by NASA system designers, and helped to identify novel as well as known problematic configurations

A concept for multi-criteria environmental assessment of aircraft trajectories

Comprehensive assessment of the environmental aspects of flight movements is of increasing interest to the aviation sector as a potential input for developing sustainable aviation strategies that consider climate impact, air quality and noise issues simultaneously. However, comprehensive assessments of all three environmental aspects do not yet exist and are in particular not yet operational practice in flight planning. The purpose of this study is to present a methodology which allows to establish a multi-criteria environmental impact assessment directly in the flight planning process. The method expands a concept developed for climate optimisation of aircraft trajectories, by representing additionally air quality and noise impacts as additional criteria or dimensions, together with climate impact of aircraft trajectory. We present the mathematical framework for environmental assessment and optimisation of aircraft trajectories. In that context we present ideas on future implementation of such advanced meteorological services into air traffic management and trajectory planning by relying on environmental change functions (ECFs). These ECFs represent environmental impact due to changes in air quality, noise and climate impact. In a case study for Europe prototype ECFs are implemented and a performance assessment of aircraft trajectories is performed for a one-day traffic sample. For a single flight fuel-optimal versus climate-optimized trajectory solution is evaluated using prototypic ECFs and identifying mitigation potential. The ultimate goal of such a concept is to make available a comprehensive assessment framework for environmental performance of aircraft operations, by providing key performance indicators on climate impact, air quality and noise, as well as a tool for environmental optimisation of aircraft trajectories. This framework would allow studying and characterising changes in traffic flows due to environmental optimisation, as well as studying trade-offs between distinct strategic measure

Key technologies for safe and autonomous drones

Drones/UAVs are able to perform air operations that are very difficult to be performed by manned aircrafts. In addition, drones' usage brings significant economic savings and environmental benefits, while reducing risks to human life. In this paper, we present key technologies that enable development of drone systems. The technologies are identified based on the usages of drones (driven by COMP4DRONES project use cases). These technologies are grouped into four categories: U-space capabilities, system functions, payloads, and tools. Also, we present the contributions of the COMP4DRONES project to improve existing technologies. These contributions aim to ease drones’ customization, and enable their safe operation.This project has received funding from the ECSEL Joint Undertaking (JU) under grant agreement No 826610. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and Spain, Austria, Belgium, Czech Republic, France, Italy, Latvia, Netherlands. The total project budget is 28,590,748.75 EUR (excluding ESIF partners), while the requested grant is 7,983,731.61 EUR to ECSEL JU, and 8,874,523.84 EUR of National and ESIF Funding. The project has been started on 1st October 2019

Third Conference on Artificial Intelligence for Space Applications, part 2

Topics relative to the application of artificial intelligence to space operations are discussed. New technologies for space station automation, design data capture, computer vision, neural nets, automatic programming, and real time applications are discussed

Projected Impact of Compositional Verification on Current and Future Aviation Safety Risk

The projected impact of compositional verification research conducted by the National Aeronautic and Space Administration System-Wide Safety and Assurance Technologies on aviation safety risk was assessed. Software and compositional verification was described. Traditional verification techniques have two major problems: testing at the prototype stage where error discovery can be quite costly and the inability to test for all potential interactions leaving some errors undetected until used by the end user. Increasingly complex and nondeterministic aviation systems are becoming too large for these tools to check and verify. Compositional verification is a "divide and conquer" solution to addressing increasingly larger and more complex systems. A review of compositional verification research being conducted by academia, industry, and Government agencies is provided. Forty-four aviation safety risks in the Biennial NextGen Safety Issues Survey were identified that could be impacted by compositional verification and grouped into five categories: automation design; system complexity; software, flight control, or equipment failure or malfunction; new technology or operations; and verification and validation. One capability, 1 research action, 5 operational improvements, and 13 enablers within the Federal Aviation Administration Joint Planning and Development Office Integrated Work Plan that could be addressed by compositional verification were identified

An overview of the V&V of Flight-Critical Systems effort at NASA

As the US is getting ready for the Next Generation (NextGen) of Air Traffic System, there is a growing concern that the current techniques for verification and validation will not be adequate for the changes to come. The JPDO (in charge of implementing NextGen) has given NASA a mandate to address the problem and it resulted in the formulation of the V&V of Flight-Critical Systems effort. This research effort is divided into four themes: argument-based safety assurance, distributed systems, authority and autonomy, and, software intensive systems. This paper presents an overview of the technologies that will address the problem

2020 NASA Technology Taxonomy

This document is an update (new photos used) of the PDF version of the 2020 NASA Technology Taxonomy that will be available to download on the OCT Public Website. The updated 2020 NASA Technology Taxonomy, or "technology dictionary", uses a technology discipline based approach that realigns like-technologies independent of their application within the NASA mission portfolio. This tool is meant to serve as a common technology discipline-based communication tool across the agency and with its partners in other government agencies, academia, industry, and across the world