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This slide deck is intended to be presented at the Digital Innovation Center for Excellence (DICE) conference May 2025. A lot of this information has already been approved through other presentations, but some information is new.
The third aviation revolution is seeking to enable transportation where users have access to immediate and flexible air travel; the users dictate trip origin, destination and timing. One of the major components of this vision is urban air mobility (UAM) for the masses. UAM means a safe and efficient system for vehicles to move passengers and cargo within a city. In order to reach UAM’s full market potential the vehicle will have to be autonomous. One of the primary challenges of autonomous flight is dealing with off-nominal events, both common and unforeseen; thus, intelligent contingency management (ICM) is one of the enabling technologies. In this context, the vehicle has to be aware of its internal state and external environment at all times, ascertain its capability and make decisions about mission completion or modification. All of these functions require data to model and assess the environment and then take actions based on these models. Necessarily, there is uncertainty associated with the data and the models generated from it. Since we are dealing with safety-critical systems, one of the main challenges of ICM is to generate sufficient data and to minimize its uncertainty to enable practical and safe decision making. We propose an overall architecture that incorporates deterministic and learning algorithms together to assess vehicle capabilities, project these into the future and make decisions on mission management level. A layered approach allows for mature parts and technologies to be integrated into early highly automated vehicles before the final state of autonomy is reached.
Military operations, counter-terrorism operations and emergency response often oblige operators and commanders to operate within distributed organizations and systems for safe and effective mission accomplishment. Tactical commanders and operators frequently encounter violent threats and critical demands on cognitive capacity and reaction time. In the future they will make decisions in situations where operational and system characteristics are highly dynamic and non-linear, i.e. minor events, decisions or actions may have serious and irreversible consequences for the entire mission. Commanders and other decision makers must manage true real time properties at all levels; individual operators, stand-alone technical systems, higher-order integrated human-machine systems and joint operations forces alike. Coping with these conditions in performance assessment, system development and operational testing is a challenge for both practitioners and researchers. This paper reports on research from which the results led to a breakthrough: An integrated approach to information-centered systems analysis to support future command and control systems research development. This approach integrates several areas of research into a coherent framework, Action Control Theory (ACT). It comprises measurement techniques and methodological advances that facilitate a more accurate and deeper understanding of the operational environment, its agents, actors and effectors, generating new and updated models. This in turn generates theoretical advances. Some good examples of successful approaches are found in the research areas of cognitive systems engineering, systems theory, and psychophysiology, and in the fields of dynamic, distributed decision making and naturalistic decision making.
An on-board autonomous exploration system that fuses data from multiple sensors, and makes decisions based on scientific goals is being developed using a series of artificial neural networks. Emphasis is placed on classifying minerals into broad geological categories by analyzing multispectral data from an imaging spectrometer. Artificial neural network architectures are being investigated for pattern matching and feature detection, information extraction, and decision making. As a first step, a stereogrammetry net extracts distance data from two gray scale stereo images. For each distance plane, the output is the probable mineral composition of the region, and a list of spectral features such as peaks, valleys, or plateaus, showing the characteristics of energy absorption and reflection. The classifier net is constructed using a grandmother cell architecture: an input layer of spectral data, an intermediate processor, and an output value. The feature detector is a three-layer feed-forward network that was developed to map input spectra to four geological classes, and will later be expanded to encompass more classes. Results from the classifier and feature detector nets will help to determine the relative importance of the region being examined with regard to current scientific goals of the system. This information is fed into a decision making neural net along with data from other sensors to decide on a plan of activity. A plan may be to examine the region at higher resolution, move closer, employ other sensors, or record an image and transmit it back to Earth.
Changing the culture of an organization is a monumental task that often takes years and has no set formula. Steps can be taken, however, to spur cultural change by creating spaces and infrastructure to serve as the initial driving force. An innovation space and a bicycle sharing (bike share) program were implemented at the National Aeronautics and Space Administration (NASA) Armstrong Flight Research Center (AFRC) (Edwards, California) with a vision toward connecting Center personnel, fostering collaboration and innovation, retaining newer employees, promoting flexibility, and improving the culture and workplace atmosphere. This paper discusses the steps taken, challenges faced, novel culture-change-focused design elements, lessons learned, acquired metrics, and how these initiated cultural change at AFRC. For both the innovation space and the bike share program, funding was negotiated and provided through the NASA Convergent Aeronautics Solutions (CAS) project, which was seeking to improve the innovation and collaboration capabilities at each of the four NASA aeronautics Centers. Key stakeholders across AFRC from upper management, facilities, safety, engineering, and procurement were identified early in the process and were consulted and included throughout execution to ensure that any encountered roadblocks could be easily navigated. Research was then conducted by attending conferences and visiting culture-changing organizations both inside and outside United States Government agencies. Distilling the research, identifying available space, and deciding on specific design elements for the space was conducted by a subset of individuals of diverse backgrounds to enable quick, effective decision-making. Decisions were made with the intent to increase usage and diversity of users of the space; care was taken to ensure a well-crafted atmosphere that would foster the desired culture change. The allocated physical space required major structural modifications, new furniture, and new capabilities that would bring people in. Decisions and desires underwent a rapid reiterative process in order to stay within budget and short deadlines, while holding firm to what was seen as fundamental elements of an innovation space. The framework for cultural change being established, the more difficult task began: incubating the desired culture. Intentional workday use of the innovation space was encouraged, and organized events coordinated in order to truly foster culture change. Such incubation supports the organic spread of culture change to all areas of AFRC. This framework was complemented and expanded by the implementation of the bike share program. Steps for implementation included bike selection based on lessons learned, creating bike stations and signage, implementing bike share rules, and building a volunteer maintenance infrastructure. A novel user-reporting feedback system at each bike station is a low-impact method of capturing usage metrics. Due to the nature of the work conducted at AFRC, the bike share program and feedback system were negotiated and vetted through various organizations including legal, safety, and operations. The innovation space and the bike share program together are an effective initial framework for innovation and collaboration. Culture change takes time, but the innovation space and the bike share program are already showing signs of making a positive impact on the AFRC workforce.
Adaptive Tactical Navigation (ATN) is a laboratory prototype of a knowledge based system to provide navigation system management and decision aiding in the next generation of tactical aircraft. ATN's purpose is to manage a set of multimode navigation equipment, dynamically selecting the best equipment to use in accordance with mission goals and phase, threat environment, equipment malfunction status, and battle damage. ATN encompasses functions as diverse as sensor data interpretation, diagnosis, and planning. Real time issues that were identified in ATN and the approaches used to address them are addressed. Functional requirements and a global architecture for the ATN system are described. Decision making with time constraints are discussed. Two subproblems are identified; making decisions with incomplete information and with limited resources. Approaches used in ATN to address real time performance are described and simulation results are discussed.
Image data is a critical component of the scientific information acquired by space missions. Compression of image data is required due to the limited bandwidth of the data transmission channel and limited memory space on the acquisition vehicle. This need becomes more pressing when dealing with multispectral data where each pixel may comprise 300 or more bytes. An autonomous, real time, on-board image analysis system for an exploratory vehicle such as a Mars Rover is developed. The completed system will be capable of interpreting image data to produce reduced representations of the image, and of making decisions regarding the importance of data based on current scientific goals. Data from multiple sources, including stereo images, color images, and multispectral data, are fused into single image representations. Analysis techniques emphasize artificial neural networks. Clusters are described by their outlines and class values. These analysis and compression techniques are coupled with decision making capacity for determining importance of each image region. Areas determined to be noise or uninteresting can be discarded in favor of more important areas. Thus limited resources for data storage and transmission are allocated to the most significant images.
The research project results in a powerful yet user friendly CROPCAST expert system for use by a client to determine the crop yield production of a certain crop field. The study is based on the facts that heuristic assessment and decision making in agriculture are significant and dominate much of agribusiness. Transfer of the expert knowledge concerning remote sensing based crop yield production into a specific expert system is the key program in this study. A knowledge base consisting of a root frame, CROP-YIELD-FORECAST, and four subframes, namely, SATELLITE, PLANT-PHYSIOLOGY, GROUND, and MODEL were developed to accommodate the production rules obtained from the domain expert. The expert system shell Personal Consultant Plus version 4.0. was used for this purpose. An external geographic program was integrated to the system. This project is the first part of a completely built expert system. The study reveals that much effort was given to the development of the rules. Such effort is inevitable if workable, efficient, and accurate rules are desired. Furthermore, abundant help statements and graphics were included. Internal and external display routines add to the visual capability of the system. The work results in a useful tool for the client for making decisions on crop yield production.
Image data is a critical component of the scientific information acquired by space missions. Compression of image data is required due to the limited bandwidth of the data transmission channel and limited memory space on the acquisition vehicle. This need becomes more pressing when dealing with multispectral data where each pixel may comprise 300 or more bytes. An autonomous, real time, on-board image analysis system for an exploratory vehicle such as a Mars Rover is developed. The completed system will be capable of interpreting image data to produce reduced representations of the image, and of making decisions regarding the importance of data based on current scientific goals. Data from multiple sources, including stereo images, color images, and multispectral data, are fused into single image representations. Analysis techniques emphasize artificial neural networks. Clusters are described by their outlines and class values. These analysis and compression techniques are coupled with decision-making capacity for determining importance of each image region. Areas determined to be noise or uninteresting can be discarded in favor of more important areas. Thus limited resources for data storage and transmission are allocated to the most significant images.
Many companies are looking for new tools and techniques to aid a design manager in making decisions that can reduce the time and cost of a design cycle. One tool that is available to aid in this decision making process is the Design Manager's Aid for Intelligent Decomposition (DeMAID). Since the initial release of DEMAID in 1989, numerous enhancements have been added to aid the design manager in saving both cost and time in a design cycle. The key enhancement is a genetic algorithm (GA) and the enhanced version is called DeMAID/GA. The GA orders the sequence of design processes to minimize the cost and time to converge to a solution. These enhancements as well as the existing features of the original version of DEMAID are described. Two sample problems are used to show how these enhancements can be applied to improve the design cycle. This report serves as a user's guide for DeMAID/GA.
With assistance from NASA s Marshall Space Flight Center, a new breed of ITS for technical training and complex problem-solving has hit the market to provide students and trainees with the decision-making skills necessary to succeed to the next level. The Task Tutor Toolkit (T3), developed by Stottler Henke Associates, Inc., of San Mateo, California, is a generic tutoring system shell and scenario authoring tool that emulates expert instructors and lowers the cost and difficulty of creating scenario-based ITS for technical training. The functionality of Stottler Henke Associates T3 far exceeds that of traditional computer-based training systems, which test factual recall and narrow skills by prompting students to answer multiple-choice or fill-inthe- blank questions. The T3, on the contrary, lets students assess situations, generate solutions, make decisions, and carry out actions in realistically complex scenarios.At the beginning of each scenario, the T3 tutoring system presents a briefing that describes the situation and the goals the students should pursue. Each scenario contains a solution template that specifies a partially-ordered sequence of action patterns that match correct sequences of student actions. During each scenario, the built-in simulator notifies the tutoring system of each student action. The T3 uses this information to evaluate the student action by comparing it with the scenario s solution template and with error rules that detect incorrect actions.
NASA is considering the development of a Cooperative Human-Adaptive Traffic Simulation (CHATS), to examine and evaluate performance of the National Airspace System (NAS) as the aviation community moves toward free flight. CHATS will be specifically oriented toward simulating strategic decision-making by airspace users and by the service provider s traffic management personnel, within the context of different airspace and rules assumptions. It will use human teams to represent these interests and make decisions, and will rely on computer modeling and simulation to calculate the impacts of these decisions. The simulation objectives will be to examine: 1. evolution of airspace users and the service provider s strategies, through adaptation to new operational environments; 2. air carriers competitive and cooperative behavior; 3. expected benefits to airspace users and the service provider as compared to the current NAS; 4. operational limitations of free flight concepts due to congestion and safety concerns. This paper describes an operational concept for CHATS, and presents a high-level functional design which would utilize a combination of existing and new models and simulation capabilities.