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NASA NTRS2019

Artificial Intelligence: Powering Human Exploration of the Moon and Mars

Artificial Intelligence (AI) is a growing field of computa- tional science techniques designed to mimic functions per- formed by people. Advancements in autonomy will depend on a portfolio of AI technologies. Automated planning and scheduling is a venerable field of study in AI, and is needed for a variety of mission planning functions. Plan execution technology is less well studied, but important for auton- omy and robotics. Specialized forms of automated reason- ing and machine learning are key technologies to enable fault management. Over the past decade, the NASA Au- tonomous Systems and Operations (ASO) project has devel- oped and demonstrated numerous autonomy enabling tech- nologies employing AI techniques. Our work has employed AI in three distinct ways to enable autonomous mission op- erations capabilities. Crew Autonomy gives astronauts tools to assist in the performance of each of these mission oper-ations functions. Vehicle System Management uses AI tech- niques to turn the astronaut's spacecraft into a robot, allow- ing it to operate when astronauts are not present, or to reduce astronaut workload. AI technology also enables Autonomous Robots as crew assistants or proxies when the crew are not present. When these capabilities are used to enable astro- nauts to operate autonomously, they must be integrated with user interfaces, introducing numerous human factors con- siderations; when these capabilities are used to enable vehi- cle system management, they must be integrated with flight software, and run on embedded processors under the control of real-time operating systems.We first describe human spaceflight mission operations capabilities. The remainder of the paper will describe the ASO project, and the development and demonstration per- formed by ASO since 2011. We will describe the AI tech- niques behind each of these demonstrations, which include a variety of symbolic automated reasoning and machine learn- ing based approaches. Finally, we conclude with an assess- ment of future development needs for AI to enable NASA's future Exploration missions.

Mission Operations↗
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 NTRS1987

The Mission Operations Planning Assistant

The Mission Operations Planning Assistant (MOPA) is a knowledge-based system developed to support the planning and scheduling of instrument activities on the Upper Atmospheric Research Satellite (UARS). The MOPA system represents and maintains instrument plans at two levels of abstraction in order to keep plans comprehensible to both UARS Principal Investigators and Command Management personnel. The hierarchical representation of plans also allows MOPA to automatically create detailed instrument activity plans from which spacecraft command loads may be generated. The MOPA system was developed on a Symbolics 3640 computer using the ZetaLisp and ART languages. MOPA's features include a textual and graphical interface for plan inspection and modification, recognition of instrument operational constraint violations during the planning process, and consistency maintenance between the different planning levels. This paper describes the current MOPA system.

Schuetzle, James G.↗
NASA NTRS1987

The mission operations planning assistant

The Mission Operations Planning Assistant (MOPA) is a knowledge-based system developed to support the planning and scheduling of instrument activities on the Upper Atmospheric Research Satellite (UARS). The MOPA system represents and maintains instrument plans at two levels of abstraction in order to keep plans comprehensible to both UARS Prinicpal Investigators and Command Management personnel. The hierarchical representation of plans also allows MOPA to automatically create detailed instrument activity plans from which spacecraft command loads may be generated. The MOPA system was developed on a Symbolics 3640 computer using the ZETALISP and ART languages. MOPA's features include a textual and graphical interface for plan inspection and modification, recognition of instrument operational constraint violations during the planning process, and consistency maintenance between the different planning levels. This paper describes the current MOPA system.

Schuetzle, James G.↗
NASA NTRS1989

Parallel plan execution with self-processing networks

A critical issue for space operations is how to develop and apply advanced automation techniques to reduce the cost and complexity of working in space. In this context, it is important to examine how recent advances in self-processing networks can be applied for planning and scheduling tasks. For this reason, the feasibility of applying self-processing network models to a variety of planning and control problems relevant to spacecraft activities is being explored. Goals are to demonstrate that self-processing methods are applicable to these problems, and that MIRRORS/II, a general purpose software environment for implementing self-processing models, is sufficiently robust to support development of a wide range of application prototypes. Using MIRRORS/II and marker passing modelling techniques, a model of the execution of a Spaceworld plan was implemented. This is a simplified model of the Voyager spacecraft which photographed Jupiter, Saturn, and their satellites. It is shown that plan execution, a task usually solved using traditional artificial intelligence (AI) techniques, can be accomplished using a self-processing network. The fact that self-processing networks were applied to other space-related tasks, in addition to the one discussed here, demonstrates the general applicability of this approach to planning and control problems relevant to spacecraft activities. It is also demonstrated that MIRRORS/II is a powerful environment for the development and evaluation of self-processing systems.

Dautrechy, C. Lynne↗
NASA NTRS1989

Parallel plan execution with self-processing networks

A critical issue for space operations is how to develop and apply advanced automation techniques to reduce the cost and complexity of working in space. In this context, it is important to examine how recent advances in self-processing networks can be applied for planning and scheduling tasks. For this reason, the feasibility of applying self-processing network models to a variety of planning and control problems relevant to spacecraft activities is being explored. Goals are to demonstrate that self-processing methods are applicable to these problems, and that MIRRORS/II, a general purpose software environment for implementing self-processing models, is sufficiently robust to support development of a wide range of application prototypes. Using MIRRORS/II and marker passing modelling techniques, a model of the execution of a Spaceworld plan was implemented. This is a simplified model of the Voyager spacecraft which photographed Jupiter, Saturn, and their satellites. It is shown that plan execution, a task usually solved using traditional artificial intelligence (AI) techniques, can be accomplished using a self-processing network. The fact that self-processing networks were applied to other space-related tasks, in addition to the one discussed here, demonstrates the general applicability of this approach to planning and control problems relevant to spacecraft activities. It is also demonstrated that MIRRORS/II is a powerful environment for the development and evaluation of self-processing systems.

D'Autrechy, C. Lynne↗
NASA NTRS1994

Third International Symposium on Artificial Intelligence, Robotics, and Automation for Space 1994

The Third International Symposium on Artificial Intelligence, Robotics, and Automation for Space (i-SAIRAS 94), held October 18-20, 1994, in Pasadena, California, was jointly sponsored by NASA, ESA, and Japan's National Space Development Agency, and was hosted by the Jet Propulsion Laboratory (JPL) of the California Institute of Technology. i-SAIRAS 94 featured presentations covering a variety of technical and programmatic topics, ranging from underlying basic technology to specific applications of artificial intelligence and robotics to space missions. i-SAIRAS 94 featured a special workshop on planning and scheduling and provided scientists, engineers, and managers with the opportunity to exchange theoretical ideas, practical results, and program plans in such areas as space mission control, space vehicle processing, data analysis, autonomous spacecraft, space robots and rovers, satellite servicing, and intelligent instruments.

Source record↗
NASA NTRS2002

An Evaluation of the ROSE System

A request-oriented scheduling engine, better known as ROSE, is under development within the Flight Projects Directorate for the purpose of planning and scheduling of the activities and resources associated with the science experiments to be performed aboard the International Space Station (ISS). ROSE is being designed to incrementally process requests from payload developers (PDs) to model and schedule the execution of their science experiments on the ISS. The novelty of the approach comes from its web-based interface permitting the PDs to define their request via the construction of a graphical model to represent their requirements. Based on an examination of the current ROSE implementation, this paper proposes several recommendations for changes to the modeling component and makes mention of other potential applications of the ROSE system.

John M. Usher↗
NASA NTRS2003

Decision-Theoretic Control of Planetary Rovers

Planetary rovers are small unmanned vehicles equipped with cameras and a variety of sensors used for scientific experiments. They must operate under tight constraints over such resources as operation time, power, storage capacity, and communication bandwidth. Moreover, the limited computational resources of the rover limit the complexity of on-line planning and scheduling. We describe two decision-theoretic approaches to maximize the productivity of planetary rovers: one based on adaptive planning and the other on hierarchical reinforcement learning. Both approaches map the problem into a Markov decision problem and attempt to solve a large part of the problem off-line, exploiting the structure of the plan and independence between plan components. We examine the advantages and limitations of these techniques and their scalability.

Zilberstein, Shlomo↗
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 NTRS1982

TOPEX satellite option study

The basic design of the fleet satellite communication spacecraft (FLTSATCOM) can easily accommodate any of the three payload options for the ocean dynamic topography experiment (TOPEX). The principal mission requirements as well as the payload accommodations and communications systems needed for launching this payload are reviewed. The existing FLTSATCOM satellite design is identified and the approaches for the proposed propulsion system are described in addition to subsystems for mechanical; power; attitude and velocity control; and telemetry, tracking and control are described. The compatability of FLTSATCOM with the launch vehicle is examined and its capabilities vs TOPEX requirements are summarized. Undetermined changes needed to meet data storage, thermal control, and area to mass ratio requirements are discussed. Cost estimates are included for budgetary and planning purposes. The availability of the described design is assessed based on the continuing production of FLTSATCOM spacecraft during the schedule span planned for TOPEX.

Source record↗
NASA NTRS2006

Control of Technology Transfer at JPL

Controlled Technology: 1) Design: preliminary or critical design data, schematics, technical flow charts, SNV code/diagnostics, logic flow diagrams, wirelist, ICDs, detailed specifications or requirements. 2) Development: constraints, computations, configurations, technical analyses, acceptance criteria, anomaly resolution, detailed test plans, detailed technical proposals. 3) Production: process or how-to: assemble, operated, repair, maintain, modify. 4) Manufacturing: technical instructions, specific parts, specific materials, specific qualities, specific processes, specific flow. 5) Operations: how-to operate, contingency or standard operating plans, Ops handbooks. 6) Repair: repair instructions, troubleshooting schemes, detailed schematics. 7) Test: specific procedures, data, analysis, detailed test plan and retest plans, detailed anomaly resolutions, detailed failure causes and corrective actions, troubleshooting, trended test data, flight readiness data. 8) Maintenance: maintenance schedules and plans, methods for regular upkeep, overhaul instructions. 9) Modification: modification instructions, upgrades kit parts, including software

Jet Propulsion Laboratory (JPL)↗
NASA NTRS2001

Challenges and Lessons Learned in the Application of Autonomy to Space Operations

NASA's Space Operations Management Office (SOMO) is working toward a goal of providing an integrated infrastructure of mission and data services for space missions undertaken by NASA enterprises. A significant portion of this effort is focused on reducing the cost of these services. We are interested in the potential of autonomy to reduce operations costs. SOMO services support space missions, but are not part of the mission objectives; therefore the level of acceptable risk is very low. In fact, SOMO could be effective ly prevented from applying autonomy if customers merely perceive it as adding risk to their mission(s). We are interested in this workshop from the standpoint of understanding what can be done to realize the potential cost savings due to autonomy while maintaining acceptable risk and serving the needs of our customers. We would like to present our lessons learned so far in adopting autonomy and automation, which we think will contribute to clarifying the challenges facing the use of such technology. SOMO provides services to a diverse and ambitious set of mission customers. Many of these missions are groundbreaking missions for which communications, data, and other operations requirements sometimes cannot be clearly articulated early in the program. This motivates a need for systems that are robust in the face of unanticipated situations so that customer missions are not unreasonably constrained or impacted by "shortcomings" in SOMO services. One of SOMO's primary goals is to realize a paradigm in which SOMO acts as a service provider to organizations that fly space missions for NASA, other government agencies, and even the commercial sector. These organizations purchase SOMO services "by the pound" as customers. We have to provide systems that are not experiments themselves, but rather stable bases from which to do bold experiments. To this end, SOMO also seeks to work closely with industry to see that robust autonomy technology gets infused into products and services for the space industry and beyond. The potential for application of these technologies spans space-based communications networks (e.g. TDRSS) and ground-based assets including communication and tracking antenna systems, data networks, and control centers. There are several problems that are candidates for the application of autonomy, if it can be made reliable enough, including: antenna control, antenna scheduling, communication link scheduling and operation, navigation, attitude determination, fault detection, isolation, and reconfiguration (for spacecraft or ground assets), and mission-level planning and scheduling. Some attempts have been made to apply autonomy and automation in these areas in the past with varying degrees of success. We will present relevant case histories and the lessons inferred from them. Combining this past experience with anticipated future needs, we can clarify the challenges that must be met in order to realize the benefits of autonomy.

Forrest, David J.↗
NASA NTRS2013

flexplan: Mission Planning System for the Lunar Reconnaissance Orbiter

flexplan is a mission planning and scheduling (MPS) tool that uses soft algorithms to define mission scheduling rules and constraints. This allows the operator to configure the tool for any mission without the need to modify or recompile code. In addition, flexplan uses an ID system to track every output on the schedule to the input from which it was generated. This allows flexplan to receive feedback as the schedules are executed, and update the status of all activities in a Web-based client. flexplan outputs include various planning reports, stored command loads for the Lunar Reconnaissance Orbiter (LRO), ephemeris loads, and pass scripts for automation.

Barnoy, Assaf↗
NASA NTRS2021

A Demand Access Paradigm for NASA’s Deep Space Network

NASA’s Deep Space Network (DSN) is the primary resource for communications and navigation for interplanetary space missions, for both NASA and partner agencies. As part of an investigation into improved efficiency and responsiveness, we have been exploring and prototyping the infusion of a ”demand access” model into the DSN scheduling process. Today, DSN is fully pre-scheduled in advance, and many users rely on a stable schedule to plan their own spacecraft activities, weeks in advance of execution. However, a new class of missions is emerging that may not be scheduled as far in advance, and may be event-driven in coming across science targets at unpredictable times. These users could take advantage of an on-demand mechanism to download data. Simulations have shown that such a mechanism could improve latency (time from data collection to download) by 2x, as well as more efficiently utilize the available DSN antennas. In this paper, we describe a prototype of a demand access process and how it addresses the challenges of co-existing with a staticallyscheduled body of missions, while providing the benefits of lower latency science data return.

Lucena, Girly↗
NASA NTRS1992

Research and technology goals and objectives for Integrated Vehicle Health Management (IVHM)

Integrated Vehicle Health Management (IVHM) is defined herein as the capability to efficiently perform checkout, testing, and monitoring of space transportation vehicles, subsystems, and components before, during, and after operational This includes the ability to perform timely status determination, diagnostics, and prognostics. IVHM must support fault-tolerant response including system/subsystem reconfiguration to prevent catastrophic failures; and IVHM must support the planning and scheduling of post-operational maintenance. The purpose of this document is to establish the rationale for IVHM and IVHM research and technology planning, and to develop technical goals and objectives. This document is prepared to provide a broad overview of IVHM for technology and advanced development activities and, more specifically, to provide a planning reference from an avionics viewpoint under the OAST Transportation Technology Program Strategic Plan.

Source record↗
NASA NTRS2006

Planning to Explore: Using a Coordinated Multisource Infrastructure to Overcome Present and Future Space Flight Planning Challenges

Few human endeavors present as much of a planning and scheduling challenge as space flight, particularly manned space flight. Just on the operational side of it, efforts of thousands of people across hundreds of organizations need to be coordinated. Numerous tasks of varying complexity and nature, from scientific to construction, need to be accomplished within limited mission time frames. Resources need to be carefully managed and contingencies worked out, often on a very short notice. From the beginning of the NASA space program, planning has been done by large teams of domain experts working months, sometimes years, to put together a single mission. This approach, while proven very reliable up to now, is becoming increasingly harder to sustain. Elevated levels of NASA space activities, from deployment of the new Crew Exploration Vehicle (CEV) and completion of the International Space Station (ISS), to the planned lunar missions and permanent lunar bases, will put an even greater strain on this largely manual process. While several attempts to automate it have been made in the past, none have fully succeeded. In this paper we describe the current NASA planning methods, outline their advantages and disadvantages, discuss the planning challenges of upcoming missions and propose a distributed planning/scheduling framework (CMMD) aimed at unifying and optimizing the planning effort. CMMD will not attempt to make the process completely automated, but rather serve in a decision support capacity for human managers and planners. It will help manage information gathering, creation of partial and consolidated schedules, inter-team negotiations, contingencies investigation, and rapid re-planning when the situation demands it. The fist area of CMMD application will be planning for Extravehicular Activities (EVA) and associated logistics. Other potential applications, not only in the space flight domain, and future research efforts will be discussed as well.

Balaban, Edward↗