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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS2002

Spacecraft autonomy using onboard processing for a SAR constellation mission

The Autonomous Sciencecraft Experiment (ASE) will fly onboard the Air Force TechSat 21 constellation of three spacecraft scheduled for launch in 2006. ASE uses onboard continuous planning, robust task and goal-based execution, model-based mode identification and reconfiguration, and onboard machine learning and pattern recognition to radically increase science return by enabling intelligent downlink selection and autonomous retargeting. In this paper we discuss how these AI technologies are synergistically integrated in a hybrid multi-layer control architecture to enable a virtual spacecruft science agent. Demonstration of these capabilities in a flight environment will open up tremendous new opportunities in planetary science, space physics, and earth science that would be unreachable without this technology.

Autonomous Sciencecraft Experiment ASE virtual spa↗
NASA NTRSDate not supplied

AMO-EXPRESS-2.5: Crew Autonomy Onboard the International Space Station

NASA is committed to landing American astronauts, including the first woman and the next man, on the Moon by 2024. Currently, the crew cannot take on all functions performed by the ground today, so the future crews will need more automation to reduce the crew workload for future missions. Of significant importance for these missions is the balance between crew autonomy and vehicle automation. The Advanced Exploration Systems (AES) Autonomous Systems and Operations (ASO) Project has been investigating the ability to evaluate crew self-scheduling and activity monitoring for future space missions. The ASO project designed the Autonomous Mission and Operations- EXpedite the PRocessing of Experiments to Space Station Rack-2.5 (AMO-EXPRESS-2.5) payload to evaluate crew self-scheduling and activity monitoring. The AMO-EXPRESS-2.5 builds on the previous AMO-EXPRESS and AMO-EXPRESS-2.0 demonstrations on ISS. The AMO-EXPRESS-2.5 demonstration goals are to prove crew self-scheduling by planning through diagnosing systems expertise, failure detection, procedure recommendation and situational awareness. This paper will describe the development, test and execution results of the AMO-EXPRESS-2.5 demonstration, and will also outline the future planned development and operational efforts to enable autonomy for future deep space manned missions.

Brooke C. Allen↗
NASA NTRSDate not supplied

Crew Autonomy Through Self-Scheduling: Guidelines for Crew Scheduling Performance Envelope and Mitigation Strategies

NASA’s future long duration exploration missions (LDEMs) will encounter increasing communication transmission delays as they move farther from Earth-based ground stations. Because crews can no longer rely on real-time support from ground planners, they will have to self-schedule their own operational timelines effectively and efficiently. To enable this, our team develops Playbook, a mission planning and scheduling tool. Our research focuses on quantifying scheduling performance using Playbook to inform the design and development of future features aimed at streamlining timeline creation. We also aim to propose standards and guidelines for autonomous crews in LDEMs. This year, we discuss preliminary results from HERA Campaign 6.

self-scheduling↗
NASA NTRS2001

Onboard autonomy on the Three Corner Sat Mission

Three Corner Sat (3CS) is a mission of three university nanosatellites scheduled for launch on September 2002. The 3CS misison will utilize significan onboard autonomy to perform onboard science data validation and replanning.

scheduling planning resources model based systems↗
NASA NTRS1990

Fundamental research in artificial intelligence at NASA

This paper describes basic research at NASA in the field of artificial intelligence. The work is conducted at the Ames Research Center and the Jet Propulsion Laboratory, primarily under the auspices of the NASA-wide Artificial Intelligence Program in the Office of Aeronautics, Exploration and Technology. The research is aimed at solving long-term NASA problems in missions operations, spacecraft autonomy, preservation of corporate knowledge about NASA missions and vehicles, and management/analysis of scientific and engineering data. From a scientific point of view, the research is broken into the categories of: planning and scheduling; machine learning; and design of and reasoning about large-scale physical systems.

Friedland, Peter↗
NASA NTRS2012

Onboard Autonomy and Ground Operations Automation for the Intelligent Payload Experiment (IPEX) CubeSat Mission

The Intelligent Payload Experiment (IPEX) is a cubesat manifested for launch in October 2013 that will flight validate autonomous operations for onboard instrument processing and product generation for the Intelligent Payload Module (IPM) of the Hyperspectral Infra-red Imager (HyspIRI) mission concept. We first describe the ground and flight operations concept for HyspIRI IPM operations. We then describe the ground and flight operations concept for the IPEX mission and how that will validate HyspIRI IPM operations. We then detail the current status of the mission and outline the schedule for future development.

project data planning↗
NASA NTRS2000

Wind Tunnel Management and Resource Optimization: A Systems Modeling Approach

Time, money, and, personnel are becoming increasingly scarce resources within government agencies due to a reduction in funding and the desire to demonstrate responsible economic efficiency. The ability of an organization to plan and schedule resources effectively can provide the necessary leverage to improve productivity, provide continuous support to all projects, and insure flexibility in a rapidly changing environment. Without adequate internal controls the organization is forced to rely on external support, waste precious resources, and risk an inefficient response to change. Management systems must be developed and applied that strive to maximize the utility of existing resources in order to achieve the goal of "faster, cheaper, better". An area of concern within NASA Langley Research Center was the scheduling, planning, and resource management of the Wind Tunnel Enterprise operations. Nine wind tunnels make up the Enterprise. Prior to this research, these wind tunnel groups did not employ a rigorous or standardized management planning system. In addition, each wind tunnel unit operated from a position of autonomy, with little coordination of clients, resources, or project control. For operating and planning purposes, each wind tunnel operating unit must balance inputs from a variety of sources. Although each unit is managed by individual Facility Operations groups, other stakeholders influence wind tunnel operations. These groups include, for example, the various researchers and clients who use the facility, the Facility System Engineering Division (FSED) tasked with wind tunnel repair and upgrade, the Langley Research Center (LaRC) Fabrication (FAB) group which fabricates repair parts and provides test model upkeep, the NASA and LARC Strategic Plans, and unscheduled use of the facilities by important clients. Expanding these influences horizontally through nine wind tunnel operations and vertically along the NASA management structure greatly increases the complexity of developing a model that can be used for successfully implementing a standardized management planning tool. The objective of this study was to implement an Integrated Wind Tunnel Planning System to improve the operations within the aeronautics testing and research group, in particular Wind Tunnel Enterprise. The study included following steps: Conducted literature search and expert discussions (NASA and Old Dominion University faculty), Performed environmental scan of NASA Langley wind tunnel operations as foundation for problem definition. Established operation requirements and evaluation methodologies. Examined windtunnel operations to map out the common characteristics, critical components, and system structure. Reviewed and evaluated various project scheduling and management systems for implementation, Evaluated and implemented "Theory of Constraints (TOC)" project scheduling methodology at NASA Langley wind tunnel operations together with NASA staff.

Jacobs, Derya, A.↗
NASA NTRS1986

Automating the U.S. Space Station's electrical power system

NASA's Lewis Research Center is developing a highly automated system for the generation, storage and distribution of electrical power aboard the projected Space Station. This autonomous power system will employ conventional algorithms, enhanced by expert systems, to schedule power, allocate energy, diagnose causes of failure, propose goals, prepare plans for their implementation, evaluate their consequences, and select optimum plans for their execution. While crew-interactive expert systems will be ready for the initial Space Station, total system autonomy is expected to require additional development time.

Dolce, James L.↗
NASA NTRS2021

Demonstrations of System-Level Autonomy for Spacecraft

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.

Prather, Maurice↗
NASA NTRSDate not supplied

Crew Autonomy Through Self-Scheduling: Operational Characterization

NASA’s future long-duration exploration missions (LDEMs) will encounter increasing communication transmission delays as they move farther from Earth-based ground stations. This necessitates a new approach, as crews can no longer rely on real-time support from ground planners and must self-schedule their own operational timelines effectively and efficiently. To enable this transition, our team has developed Playbook, a mission planning and scheduling tool. Our research focuses on quantifying scheduling performance using Playbook to inform the design and development of future features to streamline timeline creation. We also aim to propose standards and guidelines for autonomous crews in LDEMs. In the past year, we have focused on further validating and quantifying the effects of countermeasure aids on self-scheduling performance. There are two software aids in Playbook (self-scheduling platform): Suggested Fixes, which propose an edit to resolve violations within a timeline, and No-Go Zones, which highlight where activities should not be scheduled on a timeline. We have made significant progress in HERA Campaign 7 (C7) data collection, increasing the number of days crew must self-schedule from 4 to 8. As a result, almost 20% of the mission is self-scheduled by the analog astronauts. We have also started data collection on a controlled lab experiment designed to quantify performance effects due to the countermeasures. We expect to present the preliminary results from both efforts. Finally, we have conducted an exploratory analysis of NASA’s HERA Campaign 6 (C6), investigating the mission-level impacts of self-scheduling. We derived basic patterns and descriptive statistics to better characterize crew autonomy through self-scheduling. We also assessed if there are any individual indicators of preference for self-scheduling, such as experience or predilection for autonomy. Preliminary analysis indicates that the HERA C6 crew self-scheduled one out of four flexible activities, indicating unprompted adoption of self-scheduling as a concept of operation for crew autonomy.

analog↗
NASA NTRS2007

Solving the AI Planning Plus Scheduling Problem Using Model Checking via Automatic Translation from the Abstract Plan Preparation Language (APPL) to the Symbolic Analysis Laboratory (SAL)

This paper describes a translator from a new planning language named the Abstract Plan Preparation Language (APPL) to the Symbolic Analysis Laboratory (SAL) model checker. This translator has been developed in support of the Spacecraft Autonomy for Vehicles and Habitats (SAVH) project sponsored by the Exploration Technology Development Program, which is seeking to mature autonomy technology for the vehicles and operations centers of Project Constellation.

Butler, Ricky W.↗
NASA NTRS1988

A dynamic case-based planning system for space station application

We are currently investigating the use of a case-based reasoning approach to develop a dynamic planning system. The dynamic planning system (DPS) is designed to perform resource management, i.e., to efficiently schedule tasks both with and without failed components. This approach deviates from related work on scheduling and on planning in AI in several aspects. In particular, an attempt is made to equip the planner with an ability to cope with a changing environment by dynamic replanning, to handle resource constraints and feedback, and to achieve some robustness and autonomy through plan learning by dynamic memory techniques. We briefly describe the proposed architecture of DPS and its four major components: the PLANNER, the plan EXECUTOR, the dynamic REPLANNER, and the plan EVALUATOR. The planner, which is implemented in Smalltalk, is being evaluated for use in connection with the Space Station Mobile Service System (MSS).

Oppacher, F.↗
NASA NTRSDate not supplied

What is the Role of Usability and Trust in Autonomy?

Usability encompasses learnability, efficiency, memorability, effectiveness, and satisfaction. NASA’s standards for usability acceptance criteria focus on interfaces that help operators achieve their tasks efficiently, effectively, and with satisfaction. However, discussions on usability, especially regarding future highly automated and autonomous systems, rarely include trust. As NASA plans for long-duration exploration missions, it envisions astronauts operating more independently from Mission Control on Earth. This independence will drive the development of these highly automated and autonomous systems that astronauts will use daily. To prepare for this future, our team has developed a scheduling and execution software tool that facilitates self-scheduling, allowing astronauts to independently manage their own schedule without Mission Control’s involvement. Over many years, we have developed, matured, and evaluated our software tool in extreme environments, prioritizing user-centered design and high usability. These evaluations have included multiple campaigns in NASA analogs, including NEEMO, BASALT, and HERA, as well as technology demonstrations onboard the International Space Station. Our recent research on software interfaces for future astronaut autonomy revealed a strong correlation between usability and trust measures. In a controlled lab experiment, we asked novice users to perform a complex scheduling task, during which the software immediately validated the schedule’s constraints and checked for violations. We collected usability (User Experience Questionnaire, UEQ) and trust (Trust in Automated Systems scale, TAS) measures; significant, strong, and moderate correlations emerged between several of the UEQ metrics and TAS. These results support the argument for investing in usability early to enable and sustain trust in highly automated and autonomous systems.

usability↗
NASA NTRSDate not supplied

Designing Autonomy into Interfaces for Long-Duration Missions

As NASA develops technologies for long-duration crewed missions, we must understand how communication between ground control teams and astronauts differs from the current dynamic to adapt new concepts for long-duration mission operations. Today, ground control teams support astronauts with immediate availability to answer questions, resolve issues, and manage activities. In the near future, however, extended communication delays during long-duration missions will require astronauts to become more autonomous. As many of the responsibilities shift from the ground control teams to the astronauts on-board, the concept of operations must also change from how it functions today. With increased astronaut autonomy, software tools must be developed that enable efficient completion of mission tasks without increased mental workload. Designing software tools to facilitate crew autonomy requires development teams to know which data will enhance quick decision making while providing necessary context for situational awareness of systems being managed on-board. NASA's Autonomous Systems and Operations (ASO) team is presently developing a software interface tool, EXPRESS (EXpedite the PRocessing of Experiments for Space Station) 2.5 to enable a long-duration crew to schedule activities for and operate autonomous systems. This paper describes details of the integrated human factors approach that drove the design of the elements of the software tool, including self-managed scheduling, constraint-driven planning, autonomous system fault recovery, and recommended troubleshooting actions. Additionally, this paper will chronicle ASO modifications of the user interface after the team's first flight demonstration, how it was based on lessons learned during software development, as well as from crew feedback in order to develop the current version which will be demonstrated on ISS in 2021. In the upcoming ISS demonstration, the astronaut crew will be given scenarios for scheduling and operating autonomous system activities, including off-nominal scenarios and autonomous system recoveries. The demonstration of the EXPRESS 2.5 tool is a step towards improved levels of autonomy as our new journeys take us farther into space.

autonomy↗
NASA NTRSDate not supplied

Crew Autonomy through Self-Scheduling: Guidelines for Crew Scheduling Performance Envelope and Mitigation Strategies

As NASA considers long-duration exploration missions, it is envisioned that crews will behave more autonomously as compared to low-Earth orbit missions. When considering long duration exploration missions, Flight Operations Directorate (FOD) identified the following benefits and challenges presented on this slide. As missions operate further from Earth, the communication latency between the spacecraft and Mission Control Center will increase requiring the crew to take a more active role in reacting to daily planning. However, one of the key challenges for crew self-scheduling is that we do not want to overwhelm astronauts who are not expert mission planners. This leads to our primary research objective: to first, characterize the human performance envelope for the task of planning and scheduling in the context of human spaceflight operations, then, develop countermeasure aids to mitigate adverse performance effects due to plan complexity, and finally, inform performance standards and guidelines based on research results. Our next steps are to finish summarizing our findings from the experiment in conference papers and our upcoming yearly report. In the upcoming year, we will continue to collect data in analog missions and begin summarizing our recommended standards and guidelines.

user experience↗
NASA NTRS1999

Autonomous Rovers for Mars Exploration

Rovers will play a critical role in the exploration of Mars. Near-term mission plans call for long traverses over unknown terrain, robust navigation and instrument placement, and reliable operations for extended periods of time. Longer-term missions may visit multiple science sites in a single day and perform opportunistic science data collection, as well as complex scouting, construction, and maintenance tasks in preparation for an eventual human presence. The Pathfinder mission demonstrated the potential for robotic Mars exploration but at the same time indicated the need for more rover autonomy. The highly ground-intensive control with infrequent communication and high latency limited the effectiveness of the Sojourner rover. When failures occurred, Sojourner often sat idle for extended periods of time, awaiting further commands from earth. In future missions, the tasks will be more complex and extended; hence there will be even more situations where things do not go exactly as planned. Significant advances in rover autonomy are needed to cope with increasing task complexity and greater execution uncertainty. Towards this end, we have designed an on-board executive architecture that incorporates robust operation, resource utilization, and failure recovery. In addition, we have designed ground tools to produce and refine contingent schedules that take advantage of the on-board architecture's flexible execution characteristics. Together, the on-board executive and the ground tools constitute an integrated rover autonomy architecture. This work draws from our experience with the Deep Space One autonomy experiment, with enhancements to ensure robust operation in the face of the unpredictable, complex environment that the rover will encounter on Mars. The rover autonomy architecture is currently being developed and deployed on the Marsokhod rover platform at NASA Ames Research Center. The capabilities of the rover autonomy architecture to support autonomous operations will be demonstrated concretely in upcoming field tests.

Anderson, Corin↗