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The impact of infusing breakthrough autonomy technology into a flight project was a big surprise. Valuable technical and cultural lessons, many of general applicability when intorducing system-level autonomy, have been learned by infusing the Remote Agent (RA) into NASA's Deep Space 1 (DS1) Spacecraft.
This viewgraph presentation covers the following topics: Coupled Layer Architecture for Robotic Autonomy CLARAty system at the functional layer and the decision layer; and CLEaR - AI Planning and Schedule - Task Based Control, A demonstration is of the operation of the system is given, with viewgraphs of the rover movement control.
For the first time in many years, the NASA budget presented to congress calls for a focused effort on the verification and validation (V&V) of complex systems. This is mostly motivated by the results of the VVFCS (V&V of Flight-Critical Systems) study, which should materialize as a a concrete effort under the Aviation Safety program. This talk will present the results of the study, from requirements coming out of discussions with the FAA and the Joint Planning and Development Office (JPDO) to technical plan addressing the issue, and its proposed current and future V&V research agenda, which will be addressed by NASA Ames, Langley, and Dryden as well as external partners through NASA Research Announcements (NRA) calls. This agenda calls for pushing V&V earlier in the life cycle and take advantage of formal methods to increase safety and reduce cost of V&V. I will present the on-going research work (especially the four main technical areas: Safety Assurance, Distributed Systems, Authority and Autonomy, and Software-Intensive Systems), possible extensions, and how VVFCS plans on grounding the research in realistic examples, including an intended V&V test-bench based on an Integrated Modular Avionics (IMA) architecture and hosted by Dryden.
This presentation discusses the FAA belief that this research may hold the key to enabling safe autonomous operation of vehicles.
Planetary missions require significant levels of autonomy, due to the critical, one-time-only, nature of many mission objectives, and to the long delay times for human intervention. These missions also operate in highly uncertain environments, such as planetary atmospheres or surfaces. Mission Data Systems (MDS) is a project to construct a reusable software architecture for planetary missions, based on identifying the states of the mission.
The advent of the World Wide Web (Web) and the ability to easily put data repositories on-line has resulted in a proliferation of digital libraries. The heterogeneity of the underlying systems, the autonomy of the individual sites, and distributed nature of the technology has made both interoperability across the sites and the search for resources within a site major research topics. This article will describe a system that addresses both issues using standard Web protocols and meta-data labels to implement an inventory of on-line resources across a group of sites. The success of this system is strongly dependent on the existence of and adherence to a standards architecture that guides the management of meta-data within participating sites.
This presentation discusses key accomplishments of the MM-RTA project.
The reduction of aviation safety metrics below target thresholds continue to drive down the number of aviation fatalities and accidents. To meet future safety demands, sustained efforts by aviation agencies promoting safety assurance processes and systems have prompted ongoing research on identifying and mitigating in-flight risks. With the projected increase in passenger load factor and rollout of more autonomous systems into the national airspace, the need to detect high-risk events in-time or ahead-of-time is becoming increasingly crucial. New anomaly detection and precursor identification algorithms will need to scale to different airframes, levels of autonomy, and system complexity. While the pervasiveness of deep learning has resulted in the development of performant anomaly detection methods, these sophisticated models currently suffer from low end-user interpretability. Building off our previous work on identifying adverse events in multivariate flight data during descent, we propose a data-driven approach for detecting in-flight adverse events caused by the complex interplay of flight variables. Our approach utilizes ordinal patterns of important aircraft stability variables (e.g., airspeed and descent rate) to capture multivariate flight dynamics that can be used to predict the onset of unstable approaches, a high-risk adverse event that can occur during approach. Through the use of ordinal patterns, we aim to create more interpretable detection models of in-flight adverse events that can be translated to future autonomous systems without difficulty. Our analysis shows the presence of distinct ordinal pattern distributions that can be used to predict unstable approaches 1 minute ahead of time with an accuracy of 0.69 and a recall of 0.73 and 30 seconds ahead with an accuracy of 0.70 and a recall of 0.86.
The reduction of aviation safety metrics below target thresholds continue to drive down the number of aviation fatalities and accidents. To meet future safety demands, sustained efforts by aviation agencies promoting safety assurance processes and systems have prompted ongoing research on identifying and mitigating in-flight risks. With the projected increase in passenger load factor and rollout of more autonomous systems into the national airspace, the need to detect high-risk events in-time or ahead-of-time is becoming increasingly crucial. New anomaly detection and precursor identification algorithms will need to scale to different airframes, levels of autonomy, and system complexity. While the pervasiveness of deep learning has resulted in the development of performant anomaly detection methods, these sophisticated models currently suffer from low end-user interpretability. Building off our previous work on identifying adverse events in multivariate flight data during descent, we propose a data-driven approach for detecting in-flight adverse events caused by the complex interplay of flight variables. Our approach utilizes ordinal patterns of important aircraft stability variables (e.g., airspeed and descent rate) to capture multivariate flight dynamics that can be used to predict the onset of unstable approaches, a high-risk adverse event that can occur during approach. Through the use of ordinal patterns, we aim to create more interpretable detection models of in-flight adverse events that can be translated to future autonomous systems without difficulty. Our analysis shows the presence of distinct ordinal pattern distributions that can be used to predict unstable approaches 1 minute ahead of time with an accuracy of 0.69 and a recall of 0.73 and 30 seconds ahead with an accuracy of 0.70 and a recall of 0.86.
Cyberphysical systems with autonomous functions are complex pieces of software, consisting of many components, some of which implement autonomous functionality and some may use AI or machine learning algorithms. Software bugs in an autonomous system are of particular concern, as they can have catastrophic consequences. However, detailed studies based on empirical data are rare and therefore these bugs are not well understood. This paper aims to contribute towards filling that gap by investigating the software changes and bugs in Autonomy Operating System (AOS) for Unmanned Aircraft Systems (UAS), which consist of 26 components containing about 103,000 lines of code and having a total of 772 bugfixes. Based on the data extracted from the code repository and semi-structured interviews with the developers of AOS, we explore the differences among autonomous software components, components developed using Model-based Software Engineering, and reuse with respect to change proneness, fault proneness, distribution of bugfixes among AOS components and files of these components, and characteristics of bugs of different AOS components. Our results show that the autonomous components were significantly more change prone (measured in number of commits and code churn) and fault prone (measured in bugfixes per KLoC) than non-autonomous components. The distribution of the locations of bugfixes was skewed, both at component and file level (i.e., a small number of components / files contained the majority of bugs). These evidence-based findings provide important insights to researchers and practitioners alike and can be used to efficiently improve the quality and reliability of autonomous systems.
The Mission Simulation Toolkit (MST) is a flexible software system for autonomy research. It was developed as part of the Mission Simulation Facility (MSF) project that was started in 2001 to facilitate the development of autonomous planetary robotic missions. Autonomy is a key enabling factor for robotic exploration. There has been a large gap between autonomy software (at the research level), and software that is ready for insertion into near-term space missions. The MST bridges this gap by providing a simulation framework and a suite of tools for supporting research and maturation of autonomy. MST uses a distributed framework based on the High Level Architecture (HLA) standard. A key feature of the MST framework is the ability to plug in new models to replace existing ones with the same services. This enables significant simulation flexibility, particularly the mixing and control of fidelity level. In addition, the MST provides automatic code generation from robot interfaces defined with the Unified Modeling Language (UML), methods for maintaining synchronization across distributed simulation systems, XML-based robot description, and an environment server. Finally, the MSF supports a number of third-party products including dynamic models and terrain databases. Although the communication objects and some of the simulation components that are provided with this toolkit are specifically designed for terrestrial surface rovers, the MST can be applied to any other domain, such as aerial, aquatic, or space.
This slide presentation reviews some of the issues concerning verification and validation testing of autonomous spacecraft routinely culminates in the exploration of anomalous or faulted mission-like scenarios using the work involved during the Dawn mission's tests as examples. Prioritizing which scenarios to develop usually comes down to focusing on the most vulnerable areas and ensuring the best return on investment of test time. Rules-of-thumb strategies often come into play, such as injecting applicable anomalies prior to, during, and after system state changes; or, creating cases that ensure good safety-net algorithm coverage. Although experience and judgment in test selection can lead to high levels of confidence about the majority of a system's autonomy, it's likely that important test cases are overlooked. One method to fill in potential test coverage gaps is to automatically generate and execute test cases using algorithms that ensure desirable properties about the coverage. For example, generate cases for all possible fault monitors, and across all state change boundaries. Of course, the scope of coverage is determined by the test environment capabilities, where a faster-than-real-time, high-fidelity, software-only simulation would allow the broadest coverage. Even real-time systems that can be replicated and run in parallel, and that have reliable set-up and operations features provide an excellent resource for automated testing. Making detailed predictions for the outcome of such tests can be difficult, and when algorithmic means are employed to produce hundreds or even thousands of cases, generating predicts individually is impractical, and generating predicts with tools requires executable models of the design and environment that themselves require a complete test program. Therefore, evaluating the results of large number of mission scenario tests poses special challenges. A good approach to address this problem is to automatically score the results based on a range of metrics. Although the specific means of scoring depends highly on the application, the use of formal scoring - metrics has high value in identifying and prioritizing anomalies, and in presenting an overall picture of the state of the test program. In this paper we present a case study based on automatic generation and assessment of faulted test runs for the Dawn mission, and discuss its role in optimizing the allocation of resources for completing the test program.
Automated planning is a key Artificial Intelligence technology enabling Unmanned Aerial Systems (UAS) and the eminent reality of Urban Air Mobility (UAM). It produces plans, which formalize procedures often performed by humans. Plans differ from other kinds of computer programs in their ability to react and interact with a dynamically changing environment. Aviation plans must encode the procedural knowledge, reasoning capability, and capacity for multi-tasking held by competent human pilots. Correct execution of these plans (performed by software called an executive) in the dynamic airspace environment is vital to the success of each automated flight, and the safety of the vehicle and all things in its path. In the early 2000s NASA developed a plan representation language and executive called PLEXIL (Plan Execution Interchange Language) that has successfully been applied in several NASA aviation and UAS projects. Autonomy Operating System (AOS), Cockpit Hierarchical Automated Planning and Execution (CHAP-E), and ICAROUS are all projects that have used PLEXIL to help encode and automatically execute flight procedures, some normally performed by human pilots. AOS also automates a subset of pilot/Air Traffic Control communication towards enabling UAS entry into the National Airspace. PLEXIL has been open-source software since 2008 and has seen usage in a wide range of prototypical autonomy applications in academia, government, and industry. In this presentation, we describe PLEXIL and highlight its significant accomplishments in the aviation domain.
The Extravehicular Activity Retriever (EVAR) is a robotic device currently being developed by the Automation and Robotics Division at the NASA Johnson Space Center to support activities in the neighborhood of the Space Shuttle or Space Station Freedom. As the name implies, the Retriever's primary function will be to provide the capability to retrieve tools and equipment or other objects which have become detached from the spacecraft, but it will also be able to rescue a crew member who may have become inadvertently de-tethered. Later goals will include cooperative operations between a crew member and the Retriever such as fetching a tool that is required for servicing or maintenance operations. This paper documents a preliminary design for a Vision System Planner (VSP) for the EVAR that is capable of achieving visual objectives provided to it by a high level task planner. Typical commands which the task planner might issue to the VSP relate to object recognition, object location determination, and obstacle detection. Upon receiving a command from the task planner, the VSP then plans a sequence of actions to achieve the specified objective using a model-based reasoning approach. This sequence may involve choosing an appropriate sensor, selecting an algorithm to process the data, reorienting the sensor, adjusting the effective resolution of the image using lens zooming capability, and/or requesting the task planner to reposition the EVAR to obtain a different view of the object. An initial version of the Vision System Planner which realizes the above capabilities using simulated images has been implemented and tested. The remaining sections describe the architecture and capabilities of the VSP and its relationship to the high level task planner. In addition, typical plans that are generated to achieve visual goals for various scenarios are discussed. Specific topics to be addressed will include object search strategies, repositioning of the EVAR to improve the quality of information obtained from the sensors, and complementary usage of the sensors and redundant capabilities.
In the past five years, a number of automation applications for control of crew life support systems have been developed and evaluated in the Adjustable Autonomy Testbed at NASA's Johnson Space Center. This paper surveys progress on an adjustable autonomous control architecture for situations where software and human operators work together to manage anomalies and other system problems. When problems occur, the level of control autonomy can be adjusted, so that operators and software agents can work together on diagnosis and recovery. In 1997 adjustable autonomy software was developed to manage gas transfer and storage in a closed life support test. Four crewmembers lived and worked in a chamber for 91 days, with both air and water recycling. CO2 was converted to O2 by gas processing systems and wheat crops. With the automation software, significantly fewer hours were spent monitoring operations. System-level validation testing of the software by interactive hybrid simulation revealed problems both in software requirements and implementation. Since that time, we have been developing multi-agent approaches for automation software and human operators, to cooperatively control systems and manage problems. Each new capability has been tested and demonstrated in realistic dynamic anomaly scenarios, using the hybrid simulation tool.
Unmanned aerial systems (UAS), autonomy and robotics technology have been fertile ground for developing a wide variety of interdisciplinary student learning opportunities. In this talk, several projects will be described that leverage small fixed-wing UAS that have been modified to carry science payloads. These aircraft provide a unique hands-on experience for a wide range of students from college juniors to graduate students pursuing degrees in electrical engineering, aeronautical engineering, mechanical engineering, applied mathematics, physics, structural engineering and other majors. By combining rapid prototyping, design reuse and open-source philosophies, a sustainable educational program has been organized structured as full-time internships during the summer, part-time internships during the school year, short details for military cadets, and paid positions. As part of this program, every summer one or more UAS is developed from concept through design, build and test phases using the tools and facilities at the NASA Ames Research Center, ultimately obtaining statements of airworthiness and flight release from the Agency before test flights are performed. In 2016 and 2017 student projects focused on the theme of 3D printed modular airframes that may be optimized for a given mission and payload. Now in its fifth year this program has served over 35 students, and has provided a rich learning experience as they learn to rapidly develop new aircraft concepts in a highly regulated environment, on systems that will support principal investigators at university, NASA, and other US federal agencies.
We propose a new conceptual approach to system-level autonomy that exploits in a synergistic way recent breakthroughs in three specific areas: automatic generation of embeddable planning and validation software, integration of telecommunications forecaster and planning tools, and fault-tolerant assignment of computing tasks to multiple processors.
The Ground Operations Autonomous Control and Integrated Health Management plays a key role for future ground operations at NASA. The software that is integrated into this system is called G2 2011 Gensym. The purpose of this report is to describe the Ground Operations Autonomous Control and Integrated Health Management with the use of the G2 Gensym software and the G2 NASA toolkit for Integrated System Health Management (ISHM) which is a Computer Software Configuration Item (CSCI). The decision rationale for the use of the G2 platform is to develop a modular capability for ISHM and AC. Toolkit modules include knowledge bases that are generic and can be applied in any application domain module. That way, there's a maximization of reusability, maintainability, and systematic evolution, portability, and scalability. Engine modules are generic, while application modules represent the domain model of a specific application. Furthermore, the NASA toolkit, developed since 2006 (a set of modules), makes it possible to create application domain models quickly, using pre-defined objects that include sensors and components libraries for typical fluid, electrical, and mechanical systems.