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NASA NTRSDate not supplied

Advancing Autonomy in Distributed Space Systems: Insights From on-Orbit Testing with the Starling 1.0 Mission

Autonomous decision-making is crucial for enhancing mission effectiveness in Distributed Space Systems (DSS), particularly in multi-spacecraft operations where communication constraints and mission complexity pose challenges. The Distributed Spacecraft Autonomy (DSA) team at NASA’s Ames Research Center is advancing autonomy in DSS through five key technical areas: distributed resource and task management, reactive operations, system modeling and simulation, human-swarm interaction, and ad hoc network communications. The DSA experiment onboard the Starling 1.0 Mission showcases collaborative resource allocation for multi-point science data collection with four small spacecraft. Autonomy in decision-making is highlighted as a crucial factor for multi-spacecraft missions, enabling spacecraft to operate independently, reducing reliance on ground control. This capability is particularly significant for future deep-space missions, where communication delays and limited data transmission capacity make traditional command and control approaches impractical. This demonstration focuses on a GPS Channel Selection Experiment, leveraging emergent capabilities like "shared sampling" and "simultaneous sampling" to optimize channel selection across the spacecraft swarm. The experiment aims to capture ionospheric phenomena such as the Equatorial Ionization Anomaly and Polar Patches. The DSA system's autonomous reconfiguration ability is showcased, emphasizing its adaptability to natural phenomena without significant integration efforts. The GPS Channel Selection Experiment utilizes a dual-band GPS receiver to estimate plasma density in the ionosphere. Explorative and exploitative channel selections are employed based on the nature of observed phenomena. The performance of DSA algorithms is evaluated in terms of optimal channel allocations and responsiveness to changes in observed features. The DSA Flight Software utilizes the Core Flight System (cFS) framework, ensuring compatibility with the Starling 1.0 flight mission software. DSA showcases results from RTI’s Connext DDS Micro communication middleware, enabling message routing over the Ad-Hoc Network of Starling 1.0. This paper provides a comprehensive overview of the DSA experiment's initial results, emphasizing the advancements in autonomy for Distributed Space Systems and the successful collaboration with the Starling 1.0 mission.

Caleb Ashmore Adams↗

NASA NTRS1996

A Reconfigurable Testbed Environment for Spacecraft Autonomy

A key goal of NASA's New Millennium Program is the development of technology for increased spacecraft on-board autonomy. Achievement of this objective requires the development of a new class of ground-based automony testbeds that can enable the low-cost and rapid design, test, and integration of the spacecraft autonomy software. This paper describes the development of an Autonomy Testbed Environment (ATBE) for the NMP Deep Space I comet/asteroid rendezvous mission.

testbed autonomy New Millennium Program (NMP) Auto↗

NASA NTRSDate not supplied

Autonomy Technologies for Systems of a Moon Base

The aim of the workshop is to “explore emerging autonomy technologies that could enable or enhance mission capabilities, reduce mission risk, and reduce mission cost.” Enhancing mission capabilities will depend on how capable and trustworthy the autonomy implemented is. As systems increase in complexity, and also with multiple interdependent/interacting systems, current autonomy capability and trustworthiness is very low. A paradigm and technology from NASA that addresses this shortcoming will be discussed, the NASA Platform for Autonomous Systems (NPAS). NPAS also happens to address reduction of mission risk and cost. NPAS will be discussed as a capability that is reaching readiness for space use, but also serves as a reference to develop technologies suitable for integrated autonomous operations of lunar systems; encompassing autonomous systems, situational awareness, and reasoning and acting.

Autonomous systems↗

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. Some attempts have already been made to apply autonomy and automation in these areas in the past with varying degrees of success. We present brief case histories and the lessons inferred from them. Combining this past experience with anticipated future needs, we attempt to clarify the challenges that must be met in order to realize the benefits of autonomy.

challenges↗

NASA NTRS2018

System Autonomy for Space Traffic Management

This paper proposes an initial architecture for a Space Traffic Management (STM) system, based on openApplication Programming Interfaces (APIs) and drawing on previous work by the NASA Ames Research Center (ARC) to develop an architecture for low-altitude Unmanned Aerial System Traffic Management (UTM). The authors explore how autonomy could be used to enhance an STM system, and how constraints inherent in STM complicate and challenge certain applications of autonomy. We conceptually explore how autonomy could be used within an STM architecture, with multiple non-authoritative catalogs of resident space objects, and to determine which oftwo conjuncting spacecraft moves. NASA ARC is developing a software research environment for STM, along with a physical laboratory and visualization space. We invite STM stakeholders to collaborate in our infrastructure, to help inform the design of the proposed STM architecture, and to participate in the refinement and validation of its concept of operations using the software research platform.

space situational awareness↗

NASA NTRS2017

Using a Crew Resource Management Framework to Develop Human-Autonomy Teaming Measures

Recent developments in technology have permitted an increased use of autonomy. To work best with humans, autonomy should have the qualities of a good team member. But how can these qualities be measured? One way is to use similar measures to those used to measure good teams with human members. For example, the Non-Technical Skills (NOTECHS) framework measures Crew Resource Management (CRM) skills that allow pilots to work together as a team. The framework consists of skill categories, elements of those categories, and behavioral markers that demonstrate good or poor performance in the elements. This paper introduces CMSD (CooperationManagementSituation AwarenessDecision Making), a measurement system based on NOTECHS and other widely-used skill level systems, which provides quantitative measures of Human-Autonomy Teaming (HAT).

Crew Resource Manaagement↗

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

A Thinking System and Thinking Autonomy

There are many definitions of what a “Thinking System” (TS) is. The literature primarily addresses what is called “Systems Thinking.” Dr. Marie Morganelli from Southern New Hampshire University states that “Systems thinking is a holistic way to investigate factors and interactions that could contribute to a possible outcome. A mindset more than a prescribed practice, systems thinking provides an understanding of how individuals can work together in different types of teams and through that understanding, create the best possible processes to accomplish just about anything.” So, a TS is a system that is capable of “systems thinking,” as it should be able to “ … create [utilize] the best possible processes [and intelligence] to accomplish just about anything.” To achieve this capability, human-like thinking is required. A truly autonomous system must be one that is capable of human-like thinking. This paper will address “Thinking Autonomy” (TA) enabled by a “Thinking System.” It will describe an architecture with the elements required to achieve the “thinking” behavior: understanding, intellect, reason, decision, will. The paper will further describe the contents and functionality of these elements and how to implement them, including software capabilities needed. Finally, the paper will provide details of a software platform that enables TA, the NASA Platform for Autonomous Systems (NPAS), and describe implementations of thinking systems. Thinking systems will enable a fundamental change how AI and autonomy are implemented. It will change from a “brute force” approach that results in one-time implementations that are minimally intelligent or autonomous to a “thinking” approach where implementations evolve continuously and enable powerful intelligence and autonomy on systems of high complexity as well as on systems-of-systems.

Thinking systems↗

NASA NTRSDate not supplied

Hardware Autonomy for Space Infrastructure

NASA prioritizes autonomous systems development with the expectation that it will continue to drive significant improvements in human and science exploration capability. Crew operations benefit from a spectrum of machine assistance to complete replacement of dangerous or highly repetitive tasks. Many science operations have a teleoperation component, and similarly benefit from a range of autonomy implementations that make long distance applications feasible. As we consider longer duration deep space missions, we also consider higher levels of autonomy in order meet emergent safety, maintenance, and logistics needs. One of the challenges within this scope is installation and maintenance of infrastructure, such as large scale instrumentation and communications equipment, crew habitats, and operational facilities. We describe how a programmable meta-material architecture may shift the paradigm of how we design, build, and operate future space infrastructure and assets. A primary objective of this strategy is to free the design space from launch vehicle constraints and fundamentally shift how a mission is designed and conducted. This integrates advances in materials (mechanical meta-materials), manufacturing (cooperative mobile robotics), and autonomy (multi-agent planning algorithms). Engineering systems that utilize a modular and reconfiguration building block approach, such as digital communication and computation systems, currently lead in terms of size and complexity scalability. NASA is extending the benefits and flexibility of digital systems to hardware systems, to optimize materials life-cycle management and expand our space exploration mission capabilities to meet long duration and deep space infrastructure needs, in accordance with long term NASA goals of "in-space reliance" and "mass-less exploration."

In space assembly↗

NASA NTRSDate not supplied

The Role of Trust and Usability in Enabling Spaceflight Crew Autonomy

Future long duration exploration missions will require an increased use of onboard automated systems as spaceflight crews venture further than before and have longer communications delays with ground support. The design of these systems must support appropriate crew trust and have sufficient usability to enable spaceflight crew autonomy or risk being misused while crews wait to communicate with ground support. We evaluated trust & usability in our self-scheduling tool, Playbook, for crew mission timelines. Data was collected in a controlled lab experiment with 31 participants. Participants in the study conducted two tasks: scheduling, where participants were responsible for scheduling a majority of a day's operational tasks, and rescheduling, where participants were provided a schedule and asked to reschedule higher priority activities. We found a significant correlation between system trust and usability, irrespective of self-scheduling tasks. We conclude that system usability may play a bigger role in how trust is learned while conducting novel crew autonomy tasks such as self-scheduling. Future research should investigate the role of usability to encourage appropriate trust in onboard automated crew systems and enable crew autonomy.

crew autonomy↗

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↗

DOE OSTI2026

Navigating the Path to Autonomy: Real-World Lessons from an Air-Free Self-Driving Laboratory

While autonomous experimentation has promise to accelerate discovery in physcial sciences, the real-world integration of predictive models and experimentation is non-trivial. Here we describe the genesis of a self-driving laboratory (SDL) for air-sensitive chemistry at Argonne National Laboratory and demonstrate the experimental design considerations needed for high-throughput experiments before predictive models can lead to scientific discovery. Our SDL was designed to explore battery electrolyte stability. Our final SDL utilized plate readers in a glovebox with a nitrogen atmosphere to perform kinetic assays and screen hundreds of battery-relevant solvents. However, the roadmap to autonomy and airfree-friendly experimentation required the complex evaluation of several spectroscopic and chromatographic methods. The greatest experimental challenges were (a) developing long-term sampling methods that remained air-free; (b) accelerating kinetics to advance reactivity projections; and (c) ensuring labware compatibility with nonaqueous solvents used in battery chemistry. Our experiences highlight the practical gap between closed-loop aspirations and the realities of chemical discovery, offering lessons on the challenges of transferring every day laboratory workflows to autonomy. These results suggest a more realistic blueprint for autonomy in chemistry—one that balances thoughtful and realistic experimental formulation.

Robertson, Lily A.↗

NASA NTRS1984

Technologies for space station autonomy

This report presents an informal survey of experts in the field of spacecraft automation, with recommendations for which technologies should be given the greatest development attention for implementation on the initial 1990's NASA Space Station. The recommendations implemented an autonomy philosophy that was developed by the Concept Development Group's Autonomy Working Group during 1983. They were based on assessments of the technologies' likely maturity by 1987, and of their impact on recurring costs, non-recurring costs, and productivity. The three technology areas recommended for programmatic emphasis were: (1) artificial intelligence expert (knowledge based) systems and processors; (2) fault tolerant computing; and (3) high order (procedure oriented) computer languages. This report also describes other elements required for Station autonomy, including technologies for later implementation, system evolvability, and management attitudes and goals. The cost impact of various technologies is treated qualitatively, and some cases in which both the recurring and nonrecurring costs might be reduced while the crew productivity is increased, are also considered. Strong programmatic emphasis on life cycle cost and productivity is recommended.

Staehle, R. L.↗

NASA NTRS2000

Autonomy for Constellation

The newer types of space systems, which are planned for the future, are placing challenging demands for newer autonomy concepts and techniques. Motivating these challenges are resource constraints. Even though onboard computing power will surely increase in the coming years, the resource constraints associated with space-based processes will continue to be a major factor that needs to be considered when dealing with, for example, agent-based spacecraft autonomy. To realize "economical intelligence", i.e., constrained computational intelligence that can reside within a process under severe resource constraints (time, power, space, etc.), is a major goal for such space systems as the Nanosat constellations. To begin to address the new challenges, we are developing approaches to constellation autonomy with constraints in mind. Within the Agent Concepts Testbed (ACT) at the Goddard Space Flight Center we are currently developing a Nanosat-related prototype for the first of the two-step program.

Truszkowski, Walt↗

NASA NTRS2002

Virtual Engineering and Science Team - Reusable Autonomy for Spacecraft Subsystems

In this paper we address the design, development, and evaluation of the Virtual Engineering and Science Team (VEST) tool - a revolutionary way to achieve onboard subsystem/instrument autonomy. VEST directly addresses the technology needed for advanced autonomy enablers for spacecraft subsystems. It will significantly support the efficient and cost effective realization of on-board autonomy and contribute directly to realizing the concept of an intelligent autonomous spacecraft. VEST will support the evolution of a subsystem/instrument model that is probably correct and from that model the automatic generation of the code needed to support the autonomous operation of what was modeled. VEST will directly support the integration of the efforts of engineers, scientists, and software technologists. This integration of efforts will be a significant advancement over the way things are currently accomplished. The model, developed through the use of VEST, will be the basis for the physical construction of the subsystem/instrument and the generated code will support its autonomous operation once in space. The close coupling between the model and the code, in the same tool environment, will help ensure that correct and reliable operational control of the subsystem/instrument is achieved.VEST will provide a thoroughly modern interface that will allow users to easily and intuitively input subsystem/instrument requirements and visually get back the system's reaction to the correctness and compatibility of the inputs as the model evolves. User interface/interaction, logic, theorem proving, rule-based and model-based reasoning, and automatic code generation are some of the basic technologies that will be brought into play in realizing VEST.

Bailin, Sidney C.↗

NASA NTRS1998

Integration of the Remote Agent for the NASA Deep Space One Autonomy Experiment

This paper describes the integration of the Remote Agent (RA), a spacecraft autonomy system which is scheduled to control the Deep Space 1 spacecraft during a flight experiment in 1999. The RA is a reusable, model-based autonomy system that is quite different from software typically used to control an aerospace system. We describe the integration challenges we faced, how we addressed them, and the lessons learned. We focus on those aspects of integrating the RA that were either easier or more difficult than integrating a more traditional large software application because the RA is a model-based autonomous system. A number of characteristics of the RA made integration process easier. One example is the model-based nature of RA. Since the RA is model-based, most of its behavior is not hard coded into procedural program code. Instead, engineers specify high level models of the spacecraft's components from which the Remote Agent automatically derives correct system-wide behavior on the fly. This high level, modular, and declarative software description allowed some interfaces between RA components and between RA and the flight software to be automatically generated and tested for completeness against the Remote Agent's models. In addition, the Remote Agent's model-based diagnosis system automatically diagnoses when the RA models are not consistent with the behavior of the spacecraft. In flight, this feature is used to diagnose failures in the spacecraft hardware. During integration, it proved valuable in finding problems in the spacecraft simulator or flight software. In addition, when modifications are made to the spacecraft hardware or flight software, the RA models are easily changed because they only capture a description of the spacecraft. one does not have to maintain procedural code that implements the correct behavior for every expected situation. On the other hand, several features of the RA made it more difficult to integrate than typical flight software. For example, the definition of correct behavior is more difficult to specify for a system that is expected to reason about and flexibly react to its environment than for a traditional flight software system. Consequently, whenever a change is made to the RA it is more time consuming to determine if the resulting behavior is correct. We conclude the paper with a discussion of future work on the Remote Agent as well as recommendations to ease integration of similar autonomy projects.

Dorais, Gregory A.↗

NASA NTRS2003

A New Simulation Framework for Autonomy in Robotic Missions

Autonomy is a key factor in remote robotic exploration and there is significant activity addressing the application of autonomy to remote robots. It has become increasingly important to have simulation tools available to test the autonomy algorithms. While indus1;rial robotics benefits from a variety of high quality simulation tools, researchers developing autonomous software are still dependent primarily on block-world simulations. The Mission Simulation Facility I(MSF) project addresses this shortcoming with a simulation toolkit that will enable developers of autonomous control systems to test their system s performance against a set of integrated, standardized simulations of NASA mission scenarios. MSF provides a distributed architecture that connects the autonomous system to a set of simulated components replacing the robot hardware and its environment.

Flueckiger, Lorenzo↗

NASA NTRS2003

Mission Simulation Facility: Simulation Support for Autonomy Development

The Mission Simulation Facility (MSF) supports research in autonomy technology for planetary exploration vehicles. Using HLA (High Level Architecture) across distributed computers, the MSF connects users autonomy algorithms with provided or third-party simulations of robotic vehicles and planetary surface environments, including onboard components and scientific instruments. Simulation fidelity is variable to meet changing needs as autonomy technology advances in Technical Readiness Level (TRL). A virtual robot operating in a virtual environment offers numerous advantages over actual hardware, including availability, simplicity, and risk mitigation. The MSF is in use by researchers at NASA Ames Research Center (ARC) and has demonstrated basic functionality. Continuing work will support the needs of a broader user base.

Pisanich, Greg↗