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The Distributed Spacecraft Autonomy (DSA) team at NASA’s Ames Research Center is advancing autonomy in distributed space systems through five key technical areas: distributed resource and task management, reactive operations, system modeling and simulation, human-swarm interaction, and ad hoc network communications. This talk describes recent successes of the DSA experiment onboard the Starling 1.0 mission.
Weightless containerless processing for space, electromagnetic position control, force measurements and techniques, and hydrodynamics
Melting and handling of molten material in weightless space environment
Missions to be performed, station operations and functions to be carried out, and technologies anticipated during the time frame of the space station were examined in order to determine the scope of the overall information management system for the space station. This system comprises: (1) the data management system which includes onboard computer related hardware and software required to assume and exercise control of all activities performed on the station; (2) the communication system for both internal and external communications; and (3) the ground segment. Techniques used to examine the information system from a functional and performance point of view are described as well as the analyses performed to derive the architecture of both the onboard data management system and the system for internal and external communications. These architectures are then used to generate a conceptual design of the onboard elements in order to determine the physical parameters (size/weight/power) of the hardware and software. The ground segment elements are summarized.
The International Space Station (ISS) Management and Cost Evaluation Task Force (IMCE) was chartered to conduct an independent external review and assessment of the ISS cost, budget, and management. In addition, the Task Force was asked to provide recommendations that could provide maximum benefit to the U.S. taxpayers and the International Partners within the President's budget request. The Task Force has made the following principal findings: (1) The ISS Program's technical achievements to date, as represented by on-orbit capability, are extraordinary; (2) The Existing ISS Program Plan for executing the FY 02-06 budget is not credible; (3) The existing deficiencies in management structure, institutional culture, cost estimating, and program control must be acknowledged and corrected for the Program to move forward in a credible fashion; (4) Additional budget flexibility, from within the Office of Space Flight (OSF) must be provided for a credible core complete program; (5) The research support program is proceeding assuming the budget that was in place before the FY02 budget runout reduction of $1B; (6) There are opportunities to maximize research on the core station program with modest cost impact; (7) The U.S. Core Complete configuration (three person crew) as an end-state will not achieve the unique research potential of the ISS; (8) The cost estimates for the U.S.-funded enhancement options (e.g., permanent seven person crew) are not sufficiently developed to assess credibility. After these findings, the Task Force has formulated several primary recommendations which are published here and include: (1) Major changes must be made in how the ISS program is managed; (2) Additional cost reductions are required within the baseline program; (3) Additional funds must be identified and applied from the Human Space Flight budget; (4) A clearly defined program with a credible end-state, agreed to by all stakeholders, must be developed and implemented.
IPG Execution Service is a framework that reliably executes complex jobs on a computational grid, and is part of the IPG service architecture designed to support location-independent computing. The new grid service enables users to describe the platform on which they need a job to run, which allows the service to locate the desired platform, configure it for the required application, and execute the job. After a job is submitted, users can monitor it through periodic notifications, or through queries. Each job consists of a set of tasks that performs actions such as executing applications and managing data. Each task is executed based on a starting condition that is an expression of the states of other tasks. This formulation allows tasks to be executed in parallel, and also allows a user to specify tasks to execute when other tasks succeed, fail, or are canceled. The two core components of the Execution Service are the Task Database, which stores tasks that have been submitted for execution, and the Task Manager, which executes tasks in the proper order, based on the user-specified starting conditions, and avoids overloading local and remote resources while executing tasks.
This briefing was presented during the Test Site Kick Off Meeting to discuss the contract awards for Task 1 and Task 2. This briefing covered a high level overview for contract deliverables, Task 1 - UAS Traffic Management and Task 2, Live Virtual Constructive Distributed Environment.
Operators can be poor monitors of automation if they are engaged concurrently in other tasks. However, in previous studies of this phenomenon the automated task was always presented in the periphery, away from the primary manual tasks that were centrally displayed. In this study we examined whether centrally locating an automated task would boost monitoring performance during a flight-simulation task consisting of system monitoring, tracking and fuel resource management sub-tasks. Twelve nonpilot subjects were required to perform the tracking and fuel management tasks manually while watching the automated system monitoring task for occasional failures. The automation reliability was constant at 87.5% for six subjects and variable (alternating between 87.5% and 56.25%) for the other six subjects. Each subject completed four 30 min sessions over a period of 2 days. In each automation reliability condition the automation routine was disabled for the last 20 min of the fourth session in order to simulate catastrophic automation failure (0 % reliability). Monitoring for automation failure was inefficient when automation reliability was constant but not when it varied over time, replicating previous results. Furthermore, there was no evidence of resource or speed accuracy trade-off between tasks. Thus, automation-induced failures of monitoring cannot be prevented by centrally locating the automated task.
The task assigned for this internship was to develop a new tool for tracking projects, their subsystems, the leads, backups, and other employees assigned to them, as well as all the relevant information related to the employee (WBS (time charge) codes, time distribution, certifications, and assignments). Currently, this data is tracked manually using a number of different spreadsheets and other tools simultaneously by a number of different people; some of these documents are then merged into one large document. This often leads to inconsistencies and loss in data due to human error. By simplifying the process of tracking this data and aggregating it into a single tool, it is possible to significantly decrease the potential for human error and time spent collecting and checking this information. II. Objective The main objective of this internship is to develop a web-based tool using Ruby on Rails to serve as a method of easily tracking projects, subsystems, and points of contact, along with employees, their assignments, time distribution, certifications, and contact information. Additionally, this tool must be capable of generating a number of different reports based on the data collected. It was important that this tool deliver all of this information using a readable and intuitive interface.
The following tasks were prioritized: software acquisition management plan; space station flight data system architectural study; space station user data system interface; automation of software development process; automation of software testing; distributed data base management; ADA (automated data acquisition) evaluation and transition and planning; network operating system software; fault tolerant computer validation methodology for onboard data management system; systems integration; artificial intelligence/expert systems; space station data network concept; space station standard interface protocols; space station data networks systems; integrated software development facility; and language trade studies.
The objective of the Advanced Laboratory and Field Arrays (ALFA) project was to reduce the Levelized Cost of Energy (LCOE) of Marine and Hydrokinetic (MHK) energy by leveraging research, development, and testing capabilities at Oregon State University, University of Washington, and the University of Alaska, Fairbanks. ALFA is a project within the Pacific Marine Energy Center (PMEC; formerly NNMREC), a multi-institution entity with a diverse funding base that focuses on research and development for marine renewables. The ALFA project aimed to accelerate the development of next-generation arrays of wave energy conversion (WEC) and tidal energy conversion (TEC) devices through a suite of field-focused R&D activities spanning a broad range of strategic opportunity areas identified in the Funding Opportunity Announcement: • Device and/or array operation and maintenance (O&M) logistics development; • High-fidelity resource characterization and/or modeling technique development and validation; • Array-specific component technology development (e.g. moorings and foundations, transmission, and other offshore grid components); • Array performance testing and evaluation; and • Novel cost-effective environmental monitoring techniques and instrumentation testing and evaluation. The objective of the Lab Collaboration Project (LCP) was to accelerate the development of next-generation marine energy conversion systems. The LCP aimed to achieve these project objectives in collaboration with the national laboratories by: • Developing concept generation and assessment tools; • Improving access to existing testing resources; • Validating collision risk models between fish and turbines; and • Advancing analysis and simulation capabilities for wave-WEC interactions and PTO analysis in nonlinear ocean waves. The ALFA portion of the project was comprised of six overarching technical tasks: • Task 1: Debris Modeling, Detection and Mitigation; • Task 2: Autonomous Monitoring & Intervention; • Task 3: Resource Characterization for Extreme Conditions; • Task 4: Robust Models for Design of Offshore Anchoring and Mooring Systems; • Task 5: Performance Enhancement for Marine Energy Converter (MEC) Arrays; and • Task 6: Evaluating Sampling Techniques for MHK Biological Monitoring. The LCP was divided into four overarching technical tasks: • Task 7: Project Management and Reporting • Task 8: Novel Design and Assessment Methodologies for Wave Energy Converter Design (Wave- SPARC) • Task 9: Testing Access for Commercial Marine Renewable Energy Technology Developers • Task 10: Quantifying Collision Risk for Fish and Turbines • Task 11: Nonlinear Ocean Waves and PTO Control Strategy Each ALFA/LCP task listed above functioned as a separate and discreet project. A final Technical Report was written for each individual task and these reports were uploaded to OSTI, after receiving DOE approval. The following document is a compilation of each of these final, approved reports arranged as individual chapters.
This document serves as the final report for the Flight Services and Aircraft Access task order NNL14AA57T as part of NASA Environmentally Responsible Aviation (ERA) Project ITD12A+. It includes descriptions of flight test preparations and execution for the Active Flow Control (AFC) Vertical Tail and Insect Accretion and Mitigation (IAM) experiments conducted on the 757 ecoDemonstrator. For the AFC Vertical Tail, this is the culmination of efforts under two task orders. The task order was managed by Boeing Research & Technology and executed by an enterprise-wide Boeing team that included Boeing Research & Technology, Boeing Commercial Airplanes, Boeing Defense and Space and Boeing Test and Evaluation. Boeing BR&T in St. Louis was responsible for overall Boeing project management and coordination with NASA. The 757 flight test asset was provided and managed by the BCA ecoDemonstrator Program, in partnership with Stifel Aircraft Leasing and the TUI Group. With this report, all of the required deliverables related to management of this task order have been met and delivered to NASA as summarized in Table 1. In addition, this task order is part of a broader collaboration between NASA and Boeing.
The data analysis tool of choice for many Sun-Earth Connection missions is the Interactive Data Language (IDL) by ITT VIS. The increasing amount of data produced by these missions and the increasing complexity of image processing algorithms requires access to higher computing power. Parallel computing is a cost-effective way to increase the speed of computation, but algorithms oftentimes have to be modified to take advantage of parallel systems. Enhancing IDL to work on clusters gives scientists access to increased performance in a familiar programming environment. The goal of this project was to enable IDL applications to benefit from both computing clusters as well as graphics processing units (GPUs) for accelerating data analysis tasks. The tool suite developed in this project enables scientists now to solve demanding data analysis problems in IDL that previously required specialized software, and it allows them to be solved orders of magnitude faster than on conventional PCs. The tool suite consists of three components: (1) TaskDL, a software tool that simplifies the creation and management of task farms, collections of tasks that can be processed independently and require only small amounts of data communication; (2) mpiDL, a tool that allows IDL developers to use the Message Passing Interface (MPI) inside IDL for problems that require large amounts of data to be exchanged among multiple processors; and (3) GPULib, a tool that simplifies the use of GPUs as mathematical coprocessors from within IDL. mpiDL is unique in its support for the full MPI standard and its support of a broad range of MPI implementations. GPULib is unique in enabling users to take advantage of an inexpensive piece of hardware, possibly already installed in their computer, and achieve orders of magnitude faster execution time for numerically complex algorithms. TaskDL enables the simple setup and management of task farms on compute clusters. The products developed in this project have the potential to interact, so one can build a cluster of PCs, each equipped with a GPU, and use mpiDL to communicate between the nodes and GPULib to accelerate the computations on each node.
The Management Integration Panel of the Space Station Operations Task Force was chartered to provide a structure and ground rules for integrating the efforts of the other three panels and to address a number of cross cutting issues that affect all areas of space station operations. Issues addressed include operations concept implementation, alternatives development and integration process, strategic policy issues and options, and program management emphasis areas.
The author has identified the following significant results. ERTS-1 MSS data taken on October 10, 1972, of the Little Bahama Bank are being used to demonstrate the use of ERTS-1 data for mapping of shallow water features for the purpose of upgrading world navigation charts. Marked reflectance differences occur for the shallow water areas in bands 4, 5, and 6. Digital processing of two adjacent data tapes within the ERTS-1 frame covering an area of about 40 by 40 miles has been completed. Correlation of depth measurements to 5 meters has been successful. A mathematical model for depth measurements using ratio of voltages in band 4 and 5 has been successfully developed and is being tested for accuracy. Additional studies for areas near Puerto Rico and in northern Lake Michigan will be undertaken. Satellite data will also provide geographical evidence for verifying existence or nonexistence of doubtful shoal waters now appearing on world charts and considered to be hazardous to shipping.
We describe an integrated system, SkICAT (Sky Image Cataloging and Analysis Tool), for the automated reduction and analysis of the Palomar Observatory-ST ScI Digitized Sky Survey. The Survey will consist of the complete digitization of the photographic Second Palomar Observatory Sky Survey (POSS-II) in three bands, comprising nearly three Terabytes of pixel data. SkICAT applies a combination of existing packages, including FOCAS for basic image detection and measurement and SAS for database management, as well as custom software, to the task of managing this wealth of data. One of the most novel aspects of the system is its method of object classification. Using state-of-theart machine learning classification techniques (GID3* and O-BTree), we have developed a powerful method for automatically distinguishing point sources from non-point sources and artifacts, achieving comparably accurate discrimination a full magnitude fainter than in previous Schmidt plate surveys. The learning algorithms produce decision trees for classification by examining instances of objects classified by eye on both plate and higher quality CCD data. The same techniques will be applied to perform higher-level object classification (e.g., of galaxy morphology) in the near future. Another key feature of the system is the facility to integrate the catalogs from multiple plates (and portions thereof) to construct a single catalog of uniform calibration and quality down to the faintest limits of the survey. SkICAT also provides a variety of data analysis and exploration tools for the scientific utilization of the resulting catalogs. We include initial results of applying this system to measure the counts and distribution of galaxies in two bands down to Bj is approximately 21 mag over an approximate 70 square degree multi-plate field from POSS-II. SkICAT is constructed in a modular and general fashion and should be readily adaptable to other large-scale imaging surveys.
The Advanced Exploration Systems (AES) Logistics Reduction (LR) project Radio-frequency identification (RFID) Enabled Autonomous Logistics Management (REALM) task focuses on the subset of autonomous logistics management functions pertaining to automated localization and inventory of all physical assets pertaining to, or within, a vehicle utilizing RFID technologies. REALM technology can provide detailed data to enable autonomous operations such as automated crew procedure generation and robotic interaction with logistics and deep space habitats; this is especially of value where communication delays with Earth drive the need for self-reliance. The REALM project will conduct a series of ISS technology demonstrations. The first ISS demonstration, REALM-1, started in February 2017 and was completed at the end of FY19 when it was transitioned to the ISS program for sustaining operations. The second ISS demonstration, REALM-2, started late2019 and will continue for at least one year.
An experimental representation of a computer-aided multi-task flight management situation has been developed. A computer aiding program was implemented to serve as a back-up decision maker. An experiment was conducted with a balanced design of several subject runs for different workload levels. This was achieved using three levels of subsystem event arrival rates, three levels of control task involvement, and three levels of availability of computer aiding. Experimental results compared quite favorably with those from a computer simulation which employed a queueing model. It was shown that the aiding had enhanced system performance as well as subjective ratings, and that the adaptive aiding policy further reduced subsystem delay.