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Alternative mission/system approaches to automated Mars surface sample return based on utilization of Titan 3 or Saturn Intermediate-20 launch vehicles
The development of a mission simulator for use in the Terminal Configured Vehicle (TCV) program is outlined. The broad objectives of the TCV program are to evaluate new concepts in airborne systems and in operational flight procedures. These evaluations are directed toward improving terminal area capacity and efficiency, improving approach and landing capability in adverse weather, and reducing noise impact in the terminal area. A description is given of the design features and operating principles of the two major components of the TCV Mission Simulator: the TCV Aft Flight Deck Simulation and the Terminal Area Air Traffic Model Simulation, and their merger to form the TCV Mission Simulator. The first research study conducted in the Mission Simulator is presented along with some preliminary results.
Solar electric propulsion /SEP/ for automated planetary missions, discussing system characteristics, capabilities and costs
Solar electric propulsion /SEP/ for automated planetary missions, discussing system characteristics, capabilities and costs
As the Mars Curiosity Rover transmits data to the JPL Ground Data System (GDS), it frequently observes data loss and corruption, requiring re-transmits from the rover and Ground Data System Analysts (GDSA) to monitor the downlink process. As new missions are launched, the GDSA team redistributes analysts to these new missions, causing shortages in previous missions. The GDSA team can significantly benefit from the automation and optimization of the downlink process of telemetry data. In fact, there is a need for a better understanding of why the data is corrupted, so that the GDSA team can best determine the root cause of the issues in the GDS. This paper presents machine learning and deep learning based approaches to automate and optimize the detection of data loss. We first created a pipeline to automatically accumulate data from the telemetry databases (MAROS, Telemetry Data Storage, and GDS Elastic Search Database) in the downlink process. With our newly created datasets, we perform feature selection to supplement the GDSA understanding of the downlink process and provide supplemental analysis on the importance of different features. We implement various machine learning and deep learning based models, including support vector machines, ensemble methods, and deep neural networks and evaluate their accuracies in identifying whether a downlink process is complete or incomplete. We utilize fast hyperparameter optimization methods that allow our models to quickly be re-trained, allowing them to quickly be tuned and optimized on daily incoming data in real time. This hyperparameter optimization also allows our methods to be quickly integrated into other JPL missions. Our results show that our best-performing machine learning and deep learning based models outperform the existing GDSA detection software by 6 accuracy points and can aid analysts by providing insights into the data accountability problem. Since these various machine learning and deep learning approaches vary significantly in interpretability, we provide a discussion on the tradeoffs between their performance and trustworthiness in helping detect issues in data transmission.
A long-term program is in progress at JPL to reduce cost and risk of mission operations through defect prevention and error management. A major element of this program, Mission Operations and Command Assurance (MO&CA), provides a system level function on flight projects to instill quality in mission operations. MO&CA embodies the total quality management TQM principle of continuous process improvement (CPI) and uses CPI in applying automation to mission operations to reduce risk and costs. MO&CA has led efforts to apply and has implemented automation in areas that impact the daily flight project work environment including Incident Surprise Anomaly tracking and reporting; command data verification, tracking and reporting; and command support data usage. MO&CA's future work in automation will take into account that future mission operations systems must be designed to avoid increasing error through the introduction of automation, while adapting to the demands of smaller flight teams.
A challenge for any proposed mission is to demonstrate convincingly that the proposed systems will in fact deliver the science promised. Funding agencies and mission design personnel are becoming ever more skeptical of the abstractions that form the basis of the current state of the practice with respect to approximating science return. To address this, we have been using automated planning and scheduling technology to provide actual coverage campaigns that provide better predictive performance with respect to science return for a given mission design and set of mission objectives given implementation uncertainties. Specifically, we have applied an adaptation of ASPEN and SPICE to the Eagle-Eye domain that demonstrates the performance of the mission design with respect to coverage of science imaging targets that address climate change and disaster response. Eagle-Eye is an Earth-imaging telescope that has been proposed to fly aboard the International Space Station (ISS).
An automation concept for the autonomous operation of space station subsystems, i.e., electric power, thermal control, and communications and tracking are discussed. To assure that functions essential for autonomous operations are not neglected, an operations function (systems monitoring and control) is included in the discussion. It is recommended that automated speech recognition and synthesis be considered a basic mode of man/machine interaction for space station command and control, and that the data management system (DMS) and other systems on the space station be designed to accommodate fully automated fault detection, isolation, and recovery within the system monitoring function of the DMS.
Mission Design and Navigation at the Jet Propulsion Laboratory has implemented an automation framework tool to assist in orbit determination and maneuver design analysis. This paper describes the lessons learned from previous automation tools and how they have been implemented in this tool. In addition this tool has revealed challenges in software implementation, testing, and user education. This paper describes some of these challenges and invites others to share their experiences.
Mission concepts were investigated for automated return to Earth of a Mars surface sample adequate for detailed analyses in scientific laboratories. The minimum sample mass sufficient to meet scientific requirements was determined. Types of materials and supporting measurements for essential analyses are reported. A baseline trajectory profile was selected for its low energy requirements and relatively simple implementation, and trajectory profile design data were developed for 1979 and 1981 launch opportunities. Efficient spacecraft systems were conceived by utilizing existing technology where possible. Systems concepts emphasized the 1979 launch opportunity, and the applicability of results to other opportunities was assessed. It was shown that the baseline missions (return through Mars parking orbit) and some comparison missions (return after sample transfer in Mars orbit) can be accomplished by using a single Titan III E/Centaur as the launch vehicle. All missions investigated can be accomplished by use of Space Shuttle/Centaur vehicles.
The Mars 2020 Rover mission is using automated scheduling in two capacities. As of March 2021 a ground-based scheduler automatically schedules rover wake/sleep and preheats to support Mars 2020 daily operations. Included in the ground system is an explainable scheduling tool that allows users in-sight into the schedule developed by the automated scheduler. An onboard scheduler that will allow the rover autonomously better account for variations in execution is in development for use later in the mission.
The practical application of scripting languages and World Wide Web tools to the support of spacecraft ground system automation, is reported on. The mission activities and the automation tools used at the Goddard Space Flight Center (MD) are reviewed. The use of the Tool Command Language (TCL) and the Practical Extraction and Report Language (PERL) scripting tools for automating mission operations is discussed together with the application of different tools for the Compton Gamma Ray Observatory ground system.
This report describes MESA, a software environment for creating applications that automate NASA mission opterations. MESA enables intelligent automation by utilizing model-based reasoning techniques developed in the field of Artificial Intelligence. Model-based reasoning techniques are realized in Mesa through native support of causal modeling and discrete event simulation.
In 1987, NASA-Johnson's Mission-Control Center (MCC) undertook the incorporation of novel concepts and technologies for advanced automation, collectively designated the Real Time Data System, emphasizing the achievement of advanced, near-operational systems by means of a rapid interactive method. In the majority of cases, the applications have been of such high quality and usefulness that they have been promoted to operational status. A key factor in this swift implementation has been the MCC's integrated team of software engineers and flight controllers.
The Automated Rendezvous and Capture (AR&C) system was designed and tested at NASA's Marshall Space Flight Center (MSFC) to demonstrate technologies and mission strategies for automated rendezvous and docking of spacecraft in Earth orbit, The system incorporates some of the latest innovations in Global Positioning, System space navigation, laser sensor technologies and automated mission sequencing algorithms. The system's initial design and integration was completed in 1998 and has undergone testing at MSFC. This paper describes the major components of the AR&C system and presents results from the official system tests performed in MSFC's Flight Robotics Laboratory with digital simulations and hardware in the loop tests. The results show that the AR&C system can safely and reliably perform automated rendezvous and docking missions in the absence of system failures with 100 percent success. When system failures are included, the system uses its automated collision avoidance maneuver logic to recover in a safe manner. The primary objective of the AR&C project is to prove that by designing a safe and robust automated system, mission operations cost can be reduced by decreasing the personnel required for mission design, preflight planning and training required for crewed rendezvous and docking missions.
Telerobotic servicer technology requirements were identified for typical on-orbit servicing operations, including: automation requirements and automated system utilization in typical servicing missions; key automation technologies used for servicing; evolution concepts; technology development timetable; and servicing technology drivers. Teleoperation, robotics, and artificial intelligence are needed in the servicing missions investigated. Analysis shows that teleoperation will be used more widely than fully robotic systems, at least during the early space station years because of the diversity and unpredictability of many servicing tasks which call for the human operator's skills, resourcefulness, and decision-making ability. There will be heavy dependence on a sophisticated, flexible, readily accessible, high-speed and high-capacity data management system which can provide the expert system support required in diagnosing, troubleshooting, decision making, task scheduling, and mission planning.
This viewgraph presentation describes Orion mission's automation Guidance, Navigation and Control (GNC) architecture and interfaces. The contents include: 1) Orion Background; 2) Shuttle/Orion Automation Comparison; 3) Orion Mission Sequencing; 4) Orion Mission Sequencing Display Concept; and 5) Status and Forward Plans.
This paper describes the work being done at the National Aeronautics and Space Administration's Langley Research Center on the development of a mission simulator for use in the Terminal Configured Vehicle Program. A brief description of the goals and objectives of the Terminal Configured Vehicle Program is presented. A more detailed description of the Mission Simulator, in its present configuration, and its components is provided. Finally, a description of the first research study conducted in the Mission Simulator is presented along with a discussion of some preliminary results from this study.