An Overview of Structures and Materials for Future Space Vehicles

The NASA, primarily at Langley Research Center, has been conducting analytical and experimental developmental programs for high temperature structures for over 30 years. Over this time many significant technologies in both structures and materials have evolved. The structures and materials for primary airframe concepts such as metal-matrix composites, organic composites, advanced metals and carbon-carbon have emerged from laboratory curiousities to state-of-the-art structures. New manufacturing processes, such as electron beam welding, superplastic forming and diffusion bonding, allow innovative design concepts that were impracticable only a decade ago. Several of these technologies have been flight demonstrated as secondary structures such as flaps, rudders and speed brakes. Analytical techniques have improved to the point where the design and analysis of complex thermostructural concepts can be readily evaluated in relatively short time frames, when only 20 years ago these techniques were unknown. This paper reviews these technologies, proven, emerging and conceptual with emphasis on their applications to space vehicles

Advancing automation and robotics technology for the Space Station and for the US economy, volume 2

In response to Public Law 98-371, dated July 18, 1984, the NASA Advanced Technology Advisory Committee has studied automation and robotics for use in the Space Station. The Technical Report, Volume 2, provides background information on automation and robotics technologies and their potential and documents: the relevant aspects of Space Station design; representative examples of automation and robotics; applications; the state of the technology and advances needed; and considerations for technology transfer to U.S. industry and for space commercialization

NASA Flight Planning Branch Space Shuttle Lessons Learned

Planning products and procedures that allowed the mission Flight Control Teams and the Astronaut crews to plan, train and fly every Space Shuttle mission were developed by the Flight Planning Branch at the NASA Johnson Space Center in Houston, Texas. As the Space Shuttle Program came to a close, lessons learned were collected from each phase of the successful execution of these Space Shuttle missions. Specific examples of how roles and responsibilities of console positions that develop the crew and vehicle attitude timelines have been analyzed and will be discussed. Additionally, the relationships and procedural hurdles experienced through international collaboration have molded operations. These facets will be explored and related to current and future operations with the International Space Station and future vehicles. Along with these important aspects, the evolution of technology and continual improvement of data transfer tools between the Space Shuttle and ground team has also defined specific lessons used in improving the control team s effectiveness. Methodologies to communicate and transmit messages, images, and files from the Mission Control Center to the Orbiter evolved over several years. These lessons were vital in shaping the effectiveness of safe and successful mission planning and have been applied to current mission planning work in addition to being incorporated into future space flight planning. The critical lessons from all aspects of previous plan, train, and fly phases of Space Shuttle flight missions are not only documented in this paper, but are also discussed regarding how they pertain to changes in process and consideration for future space flight planning

The 1990 progress report and future plans

This document describes the progress and plans of the Artificial Intelligence Research Branch (RIA) at ARC in 1990. Activities span a range from basic scientific research to engineering development and to fielded NASA applications, particularly those applications that are enabled by basic research carried out at RIA. Work is conducted in-house and through collaborative partners in academia and industry. Our major focus is on a limited number of research themes with a dual commitment to technical excellence and proven applicability to NASA short, medium, and long-term problems. RIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at JPL and AI applications groups at all NASA centers

Artificial Intelligence Research Branch future plans

This report contains information on the activities of the Artificial Intelligence Research Branch (FIA) at NASA Ames Research Center (ARC) in 1992, as well as planned work in 1993. These activities span a range from basic scientific research through engineering development to fielded NASA applications, particularly those applications that are enabled by basic research carried out in FIA. Work is conducted in-house and through collaborative partners in academia and industry. All of our work has research themes with a dual commitment to technical excellence and applicability to NASA short, medium, and long-term problems. FIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at the Jet Propulsion Laboratory (JPL) and AI applications groups throughout all NASA centers. This report is organized along three major research themes: (1) Planning and Scheduling: deciding on a sequence of actions to achieve a set of complex goals and determining when to execute those actions and how to allocate resources to carry them out; (2) Machine Learning: techniques for forming theories about natural and man-made phenomena; and for improving the problem-solving performance of computational systems over time; and (3) Research on the acquisition, representation, and utilization of knowledge in support of diagnosis design of engineered systems and analysis of actual systems

Understanding Algorithm Performance on an Oversubscribed Scheduling Application

The best performing algorithms for a particular oversubscribed scheduling application, Air Force Satellite Control Network (AFSCN) scheduling, appear to have little in common. Yet, through careful experimentation and modeling of performance in real problem instances, we can relate characteristics of the best algorithms to characteristics of the application. In particular, we find that plateaus dominate the search spaces (thus favoring algorithms that make larger changes to solutions) and that some randomization in exploration is critical to good performance (due to the lack of gradient information on the plateaus). Based on our explanations of algorithm performance, we develop a new algorithm that combines characteristics of the best performers; the new algorithms performance is better than the previous best. We show how hypothesis driven experimentation and search modeling can both explain algorithm performance and motivate the design of a new algorithm

Airlift scheduling for the upgraded command and control system of military airlift command.

April 1984This report describes a conceptual design for automation of the scheduling of airlift activities as part of the current upgrade of the MAC C2 System. It defines the airlift scheduling problem in generic terms before reviewing the current procedures used by MAC; and then a new scheduling system aimed at handling a very busy and dynamic wartime scenario, is introduced. The new system proposes "Airlift Scheduling Workstations" where MAC Airlift Schedulers would be able to manipulate symbolic information on a computer display to create and quickly modify schedules for aircraft, crews, and stations. For certain sub-problems in generating schedules, automated decision support algorithms would be used interactively to speed the search for feasible and efficient solutions. Airlift Scheduling Workstations are proposed to exist at each "Scheduling Cell", a conceptual organizational unit which has been given sole and complete responsibility for developing the schedule of activities for a specific set of airlift resources-aircraft by tail number, aircrew by name, and stations by location. A Mission Scheduling Database is located at each cell to support the Airlift Scheduling Workstation, and requires information communicated by Airlift Task Planners, and, Airlift Operators at many other locations. These locations would have smaller workstations with local databases, and database management software to assist Task Planners and Operators in viewing current committed and planned schedule information of particular interest to them, and to allow them to send information to the Mission Scheduling Database. The Command and Control processes for Airlift have been structured into a three level hierarchy in this report: Task Planning, Mission Scheduling, and Schedule Execution. Task Planners deal with Airlift Users and Mission Schedulers, but not Airlift Operators. Task Planning has three sub-processes: Processing User Requests; Assigning Requirements and Resources; and Monitoring Task Status. Task planning does not create missions, schedule the missions, or route aircraft. Mission Schedulers deal with Task Planners and Airlift Operators, but not Airlift Users. Mission Scheduling combines several sub-processes to allow efficient schedules to be quickly generated at the ASW (Airlift Scheduling Workstation). These sub-processes are: Mission Generation, Schedule Map Generation (for each type of aircraft), Crew Mission Sequence Generation, Station Schedule Generation, Management of Schedule Status, and Monitoring Schedule Execution and Resource Status. It is important that all these processes be co-located and processed by the Airlift Scheduling Cell. Schedule Execution is performed by Airlift Operators assigned by the scheduling process. It has three sub-processes: Monitor Assigned Schedules, Report Resources Assigned to Schedule, Report Local Capability Status. The assignment of local resources such as aircraft by tail, and crew by name is actually another scheduling process, but has not been studied in this report. Airlift Operators do not deal with Task Planners, but may deal with Airlift Users to finalize details of the scheduled operations. This three level hierarchy is compatible with the current organizational structures of MAC Command and Control. However, it is clear that both the current organizational structures and procedures of MAC Command and Control for both tactical and strategic airlift will be significantly affected by the introduction of the automated scheduling systems envisioned here. These changes will occur in an evolutionary manner after the upgraded MAC C2 system is introduced.Prepared for the Electronic Systems Division, Air Force Systems Command, USAF, Hanscom Air Force Base, Bedford, M

Space station data system analysis/architecture study. Task 2: Options development, DR-5. Volume 3: Programmatic options

Task 2 in the Space Station Data System (SSDS) Analysis/Architecture Study is the development of an information base that will support the conduct of trade studies and provide sufficient data to make design/programmatic decisions. This volume identifies the preferred options in the programmatic category and characterizes these options with respect to performance attributes, constraints, costs, and risks. The programmatic category includes methods used to administrate/manage the development, operation and maintenance of the SSDS. The specific areas discussed include standardization/commonality; systems management; and systems development, including hardware procurement, software development and system integration, test and verification

Fourth Conference on Artificial Intelligence for Space Applications

Proceedings of a conference held in Huntsville, Alabama, on November 15-16, 1988. The Fourth Conference on Artificial Intelligence for Space Applications brings together diverse technical and scientific work in order to help those who employ AI methods in space applications to identify common goals and to address issues of general interest in the AI community. Topics include the following: space applications of expert systems in fault diagnostics, in telemetry monitoring and data collection, in design and systems integration; and in planning and scheduling; knowledge representation, capture, verification, and management; robotics and vision; adaptive learning; and automatic programming