Returning to the Moon: Plans for Distributed Simulation in the Artemis Program
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This study's objective is the performance evaluation of the existing CAD/CAE (Computer Aided Design/Computer Aided Engineering) network at NASA/KSC. This evaluation also includes a similar study of the Office Automation network, since it is being planned to integrate this network into the CAD/CAE network. The Microsoft mail facility which is presently on the CAD/CAE network was monitored to determine its present usage. This performance evaluation of the various networks will aid the NASA/KSC network managers in planning for the integration of future workload requirements into the CAD/CAE network and determining the effectiveness of the planned FDDI (Fiber Distributed Data Interface) migration.
The problem of robot control is viewed at the level of communicating high-level commands produced by intelligent algorithms to the actuator/sensor controllers. Four topics are considered in the design of an integrated control and communications scheme for an intelligent robotic system: the use of abstraction spaces, hierarchical versus heterarchical control, distributed processing, and the interleaving of the steps of plan creation and plan execution. A scheme is presented for an n-level distributed hierarchical/heterarchical control system that effectively interleaves intelligent planning, execution, and sensory feedback. A three-level version of this scheme has been successfully implemented in the Intelligent Systems Research Lab at NASA Langley Research Center. This implementation forms the control structure for DAISIE (Distributed Artificially Intelligent System for Interacting with the Environment), a testbed system integrating AI software with robotics hardware.
The investigations of pressure distributions are presented for aeroloads analysis at Mach numbers from 1.55 through 2.5. Angles of attack and sideslip varied from -6 to +6 degrees. Photographs of wind tunnel models are shown.
Power system management is a process of guiding a power system towards the objective of continuous supply of electrical power to a set of loads. Spacecraft power system management requires planning and scheduling, since electrical power is a scarce resource in space. The automation of power system management for future spacecraft has been recognized as an important R&D goal. Several automation technologies have emerged including the use of expert systems for automating human problem solving capabilities such as rule based expert system for fault diagnosis and load scheduling. It is questionable whether current generation expert system technology is applicable for power system management in space. The objective of the ADEPTS (ADvanced Electrical Power management Techniques for Space systems) is to study new techniques for power management automation. These techniques involve integrating current expert system technology with that of parallel and distributed computing, as well as a distributed, object-oriented approach to software design. The focus of the current study is the integration of new procedures for automatically planning and scheduling loads with procedures for performing fault diagnosis and control. The objective is the concurrent execution of both sets of tasks on separate transputer processors, thus adding parallelism to the overall management process.
Knowledge-based technologies have been applied successfully to automate planning and scheduling in many problem domains. Automation of decision support can be increased further by integrating task-specific applications with supporting database systems, and by coordinating interactions between such tools to facilitate collaborative activities. Unfortunately, the technical obstacles that must be overcome to achieve this vision of transparent, cooperative problem-solving are daunting. Intelligent decision support tools are typically developed for standalone use, rely on incompatible, task-specific representational models and application programming interfaces (API's), and run on heterogeneous computing platforms. Getting such applications to interact freely calls for platform independent capabilities for distributed communication, as well as tools for mapping information across disparate representations. Symbiotics is developing a layered set of software tools (called NetWorks! for integrating and coordinating heterogeneous distributed applications. he top layer of tools consists of an extensible set of generic, programmable coordination services. Developers access these services via high-level API's to implement the desired interactions between distributed applications.
The payload operations support for the International Space Station (ISS) payload is reported on, describing payload activity planning, payload operations control, payload data management and overall operations integration. The operations concept employed is based on the distribution of the payload operations responsibility between the researchers and ISS partners. The long duration nature of the ISS mission dictates the geographical distribution of the payload operations activities between the different national centers. The coordination and integration of these operations will be assured by NASA's Payload Operations Integration Center (POIC). The prime objective of the POIC is the achievement of unified operations through communication and collaboration.
In regions undergoing rapid urbanization, such as West Africa, land use planning (LUP) is vital to accommodate growing population and manage natural resources. Suitability analysis modeling is a widely used tool in LUP to determine the extent to which a land area is suitable for a designated purpose, but there is a gap in the integration of remote sensing time series data into land use decisions. The goal of this study was to incorporate remote sensing time series information with suitability analyses to inform LUP decisions in urban areas. In the study area of Kumasi, Ghana, land cover trends and land surface temperature (LST) from 2000 to 2019 were used to understand climate change trends. Suitability analyses determined the fitness of land areas for predetermined uses. These background processes informed a genetic algorithm to project plausible futures for three land use scenarios. One scenario represented current land use planning practices for addressing population growth, another scenario prioritized minimizing climate change impacts while also accommodating population growth, and the final scenario focused on both of these climate and population goals in addition to high density urban development. Each of these scenarios was successful in achieving population accommodation and respective climate change mitigation goals. The results for these scenarios provide insight into plausible land use distributions in 2050 based on different planning approaches. The genetic algorithm was able to effectively develop results for each scenario through the integration of remotely sensed trends and suitability models, providing a novel approach to land use decision-making.
As the National Aeronautics and Space Administration continues to define candidate architectures for the planned lunar “Gateway”, it will be necessary to have a detailed understanding of how the crew will inhabit, operate, and maintain the spacecraft. The nature of the Gateway vehicle systems configuration and operations will have a direct impact on the scope of work activities required of the crew. Crew work schedules are sensitive to variations in spacecraft architecture, visiting vehicle activities, and logistics operations – particularly within short duration missions as initially planned for the lunar Gateway. These system and operational configurations must be taken into account when planning for crew time availability to conduct science activities on Gateway missions. This paper presents a methodology that is used to predict crew time distributions for lunar Gateway missions, as applied in NASA’s Exploration Crew Time Model (ECTM). The process utilized for evaluating crew time distributions is based on the categorization of all crew activities into a standardized ontology. Historical ISS daily crew timeline data from July 20, 2011 (post STS retirement) to present day was captured via the Operational Planning Timeline Integration System (OPTimIS) database and characterized according to the standardized ontology. This process enabled correlation and statistical analysis of the ISS data according to common mission parameters such as crew size, ECLSS system design, vehicle traffic operations, and logistics delivery operations. The results of the statistical analysis are a set of crew time distributions for each activity category. These distributions are then utilized within the ECTM to examine crew time allocations based on mission parameter inputs, which serve to characterize the Gateway mission configurations. Results for predicted crew time allocations for representative short duration Gateway missions are presented. These results can be used to evaluate crew schedule availability for science and utilization activities. Variations in expected mission architectures and mission operations are accounted for to correct crew time predictions. The analysis is being leveraged to plan utilization capability objectives that are achievable on the Gateway missions, as well as inform the viability of various mission architecture options.
This study had as an objective the performance evaluation of the existing CAD/CAE graphics network at NASA/KSC. This evaluation will also aid in projecting planned expansions, such as the Space Station project on the existing CAD/CAE network. The objectives were achieved by collecting packet traffic on the various integrated sub-networks. This included items, such as total number of packets on the various subnetworks, source/destination of packets, percent utilization of network capacity, peak traffic rates, and packet size distribution. The NASA/KSC LAN was stressed to determine the useable bandwidth of the Ethernet network and an average design station workload was used to project the increased traffic on the existing network and the planned T1 link. This performance evaluation of the network will aid the NASA/KSC network managers in planning for the integration of future workload requirements into the existing network.
The Microwave Anisotropy Probe (MAP) mission is the second mission in NASA's Office of Space Science low-cost, Medium-class Explorers (MIDEX) program. The Explorers Program is designed to accomplish frequent, low cost, high quality space science investigations utilizing innovative, streamlined, efficient management, design and operations approaches. The MAP spacecraft will produce an accurate full-sky map of the cosmic microwave background temperature fluctuations with high sensitivity and angular resolution. The MAP spacecraft is planned for launch in early 2001, and will be staffed by only single-shift operations. During the rest of the time the spacecraft must be operated autonomously, with personnel available only on an on-call basis. Four (4) innovations will work cooperatively to enable a significant reduction in operations costs for the MAP spacecraft. First, the use of a common ground system for Spacecraft Integration and Test (I&T) as well as Operations. Second, the use of Finite State Modeling for intelligent autonomy. Third, the integration of a graphical planning engine to drive the autonomous systems without an intermediate manual step. And fourth, the ability for distributed operations via Web and pager access.
Enhanced AutoNav (ENav), the baseline surface navigation software for NASA’s Perseverance rover, sorts a list of candidate paths for the rover to traverse, then uses the Approximate Clearance Evaluation (ACE) algorithm to evaluate whether the most highly ranked paths are safe. ACE is crucial for maintaining the safety of the rover, but is computationally expensive. If the most promising candidates in the list of paths are all found to be infeasible, ENav must continue to search the list and run time-consuming ACE evaluations until a feasible path is found. In this paper, we present two heuristics that, given a terrain heightmap around the rover, produce cost estimates that more effectively rank the candidate paths before ACE evaluation. The first heuristic uses Sobel operators and convolution to incorporate the cost of traversing high-gradient terrain. The second heuristic uses a machine learning (ML) model to predict areas that will be deemed untraversable by ACE. We used physics simulations to collect training data for the ML model and to run Monte Carlo trials to quantify navigation performance across a variety of terrains with various slopes and rock distributions. Compared to ENav's baseline performance, integrating the heuristics can lead to a significant reduction in ACE evaluations and average computation time per planning cycle, increase path efficiency, and maintain or improve the rate of successful traverses. This strategy of targeting specific bottlenecks with ML while maintaining the original ACE safety checks provides an example of how ML can be infused into planetary science missions and other safety-critical software.
System-level autonomy refers to autonomously meeting the crosscutting needs of a system through awareness and coordinated control spanning the system's breadth of capabilities. In contrast to function-level autonomy, which focuses on capabilities required to achieve a specific function such as surface navigation or image recognition, system-level autonomy addresses the needs to coordinate and manage activities and resources, and estimate the state, across subsystems. This paper describes demonstrations that were conducted on a spacecraft workstation testbed. The autonomy was provided by system-level planning and execution integrated with system-level estimators of orbit knowledge and spacecraft hardware health. These components are embedded in a system-level framework defining how goals are formed and executed, which elements exist, and how control authority is distributed among components. The planning and execution system at the heart of the framework has the capability to schedule, execute and monitor completion of tasks, as well as plan around unexpected events including new science opportunities and anomalies. The planning and scheduling system is the Multi-mission EXECutive (MEXEC), supported by the system-level health state estimator Model-Based Off-Nominal State Identification and Detection (MONSID), and Autonomous Navigation (AutoNav) algorithms, which determine the orbital system state based on optical observation of other targets. These components are applicable to many kinds of missions on different platforms. These demonstrations were elaborations of earlier experiments conducted on the ASTERIA (Arcsecond Space Telescope Enabling Research In Astrophysics) CubeSat, described in a companion submission [1]. The spacecraft’s extended mission served as an in-flight test platform, during which some individual autonomous capabilities were flown successfully. The autonomy experiments described here were performed on the ASTERIA workstation testbed.
This paper presents the development of ICAROUS-2 (Independent Configurable Architecture for Reliable Operation of Unmanned Systems with Distributed Onboard Services), the second generation of a software architecture that integrates several algorithms as distributed onboard services to enable robust autonomous UAS applications. In particular, the ICAROUS architecture defines a framework to perform detect and avoid, geofencing, path monitoring, path planning, and autonomous decision making to ensure safety and mission progress. Most of the core algorithms implemented in ICAROUS are formally verified using an interactive theorem prover. These algorithms are composed together using a plan execution engine, whose operational semantics is formally specified. A description of the integrated architecture, services currently available, and flight test results highlighting the capability of ICAROUS are presented.
This paper presents the development of ICAROUS-2 (Independent Configurable Architecture for Reliable Operation of Unmanned Systems with Distributed Onboard Services), the second generation of a software architecture that integrates several algorithms as distributed onboard services to enable robust autonomous UAS applications. In particular, the ICAROUS architecture defines a framework to perform detect and avoid, geofencing, path monitoring, path planning, and autonomous decision making to ensure safety and mission progress. Most of the core algorithms implemented in ICAROUS are formally verified using an interactive theorem prover. These algorithms are composed together using a plan execution engine, whose operational semantics is formally specified. A description of the integrated architecture, services currently available, and flight test results highlighting the capability of ICAROUS are presented.
The Space Technology 7 (ST-7) payload, flying on the Laser Interferometer Space Antenna (LISA) Pathfinder (LPF) mission, will demonstrate drag-free control of a test mass with acceleration disturbances below 3x10-14 m/s2/(square root)Hz. Low frequency acceleration noise introduced by the electrostatic force needed to counter static mass distribution imbalance is expected to be a significant contributor to the acceleration noise budget. For this reason, the self-gravity (due to mass imbalance) is minimized by adding trim mass to bring the total differential acceleration between the two test masses due to self-gravity below 5x10-10 m/s2 in any axis and the DC acceleration gradient due to self-gravity below 4x10-8 m/s2/m in any axis of either test mass. A plan has been established to develop the distribution and placement of the compensation masses. Compensation for the self-gravity effects on the two test masses is handled in a two step process. A nominal compensation mass is defined and incorporated early and is located very near the test masses. The final trimming for self-gravity occurs after the integration on the spacecraft with small mass added externally to the test mass vacuum enclosures. The plan identifies three preliminary points in the hardware maturity where the trimming to the as-built configuration can take place: (1) during build-up of the sensor vacuum enclosure, (2) prior to delivery of the integrated ST-7 to Europe, and (3) prior to environmental testing of the integrated LPF system. The sensitivity of the self-gravity to knowledge errors in the actual mass distribution is taken into account in the determination of final trimming opportunities and mounting locations.
As with all NASA facilities, the announcement of reduced budgets, reduced staffing, and the desire to implement smaller/quicker/cheaper missions has required the Agency's organizations to become more efficient in what they do. To accomplish these objectives, the FDD has initiated the development of the Flight Dynamics Distributed System (FDDS). The underlying philosophy of FDDS is to build an integrated system that breaks down the traditional barriers of attitude, mission planning, and navigation support software to provide a uniform approach to flight dynamics applications. Through the application of open systems concepts and state-of-the-art technologies, including object-oriented specification concepts, object-oriented software, and common user interface, communications, data management, and executive services, the FDD will reengineer most of its six million lines of code.
The first meeting of the Behavioral Health and Performance (BHP) Standing Review Panel (SRP) was held in Houston, TX on November 1-3, 2009. Our task was to assess the Integrated Research Plan (IRP) related to the fields covered in the SRP charge (see section VIII). Having considered and discussed the extensive materials distributed prior to the meeting, and the highly informative briefings by the NASA BHP Human Research Program (HRP) personnel during the site visit, the SRP agreed that the IRP is comprehensive and was developed carefully. Question and answer periods with the presenters were particularly productive and helpful to the SRP. The presenters' willingness to share information and positive responses to the SRP's suggestions were greatly appreciated. Although the IRP and related documents are impressive, the SRP does have a number of recommendations regarding both the overall plan and its component parts.