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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

Recommendations to Advance Space Trusted Autonomy

The interagency Space Science and Technology Partnership Forum was established in2015 to identify synergistic efforts and technologies across the U.S. government. While the various space agencies of the U.S. government have distinctly different visions for future operational space systems, all share important foundational common needs. These needs, combined with the maturation of autonomous technology and the prospect of leveraging autonomous systems to address those needs, have led each agency to consider how and when to to implement increasing levels of autonomy in their space systems, and how to determine the trustworthiness of an autonomous system. The Partnership facilitated dialogue among the partners, collected and analyzed data on current and desired future levels of capability, and identified gaps to motivate three recommendations that can be addressed within the Partnership community. These recommendations address the need for more robust documenting and socializing of anomalies in space system operations; the need to expand communication and trust within the community of developers, operators, and end users; and the need for a safe development and testing environment for maturing and demonstrating future autonomous space systems. These recommendations will facilitate both near-term programmatic actions and long-term steps for implementing enduring progress towards enabling space trusted autonomy.

Christopher A Jones↗
NASA NTRS2020

Supervised Autonomy for Communication-Degraded Subterranean Exploration by a Robot Team

The importance of autonomy in robotics is magnified when the robots need to be deployed and operated in areas that are too dangerous or not accessible for humans, ranging from disaster areas (to assist in emergency situations) to Mars exploration (to uncover the mystery of our neighboring planet). The DARPA Subterranean (SubT) Challenge presents a great opportunity and a formidable robotics challenge to foster such technological advancement for operations in extreme and underground environments. Robot teams are expected to rapidly map, navigate, and search underground environments including natural cave networks, tunnel systems, and urban underground infrastructure. Subterranean environments pose significant challenges for manned and unmanned operations due to limited situational awareness. In the first phase of the DARPA Subterranean Challenge (held in August 2019; targeting underground tunnels and mines), Team CoSTAR, led by NASA JPL, placed second among 11 teams across the world, accurately mapping several kilometers of two mine systems and localizing 17 target objects in the course of four one-hour missions. While the main goal of Team CoSTAR at the end of this threeyear challenge (August 2021) is a fully autonomous robotic solution, this paper describes Team CoSTAR’s results in the first phase of the challenge (August 2019), focusing on supervised autonomy of a multi-robot team under severe communication constraints. This paper also presents the design and initial results obtained from field test campaigns conducted in various tunnel-like environments, leading to the competition.

Otsu, Kyohei↗
NASA NTRS2022

Science Autonomy for Ocean Worlds Astrobiology: A Perspective

Astrobiology missions to ocean worlds in our solar system must overcome both scientific and technological challenges due to extreme temperature and radiation conditions, long communication times, and limited bandwidth. While such tools could not replace ground-based analysis by science and engineering teams, machine learning algorithms could enhance the science return of these missions through development of autonomous science capabilities. Examples of science autonomy include onboard data analysis and subsequent instrument optimization, data prioritization (for transmission), and real-time decision-making based on data analysis. Similar advances could be made to develop streamlined data processing software for rapid ground-based analyses. Here we discuss several ways machine learning and autonomy could be used for astrobiology missions, including landing site selection, prioritization and targeting of samples, classification of “features” (e.g., proposed biosignatures) and novelties (uncharacterized, “new” features, which may be of most interest to agnostic astrobiological investigations), and data transmission.

ocean worlds↗
NASA NTRS2023

An Overview of Distributed Spacecraft Autonomy at NASA Ames

Autonomous decision-making significantly increases mission effectiveness by mitigating the effects of communication constraints, like latency and bandwidth, and mission complexity on multi-spacecraft operations. To advance the state of the art in autonomous Distributed Space Systems (DSS), the Distributed Spacecraft Autonomy (DSA) team at NASA's Ames Research Center is developing within five relevant technical areas: distributed resource and task management, reactive operations, system modeling and simulation, human-swarm interaction, and ad hoc network communications. DSA is maturing these technologies - critical for future large autonomous DSS - from concept to launch via simulation studies and orbital deployments. A 100-node heterogenous Processor-in-the-Loop (PiL) testbed aids distributed autonomy capability development and verification of multi-spacecraft missions. The DSA software payload deployed to the D-Orbit SCV-004 spacecraft demonstrates multi-agent reconfigurability and reliability as part of an ESA-sponsored in-orbit technology demonstration. Finally, DSA's primary flight mission showcases collaborative resource allocation for multipoint science data collection with four small spacecraft as a payload on NASA's Starling 1.0 satellites.

Caleb Ashmore Adams↗
NASA NTRSDate not supplied

Gateway Autonomy for Enabling Deep Space Exploration

The Gateway spacecraft is an important stepping-stone to exploration of the solar system, integrating commercial and international partners into a tightly coupled system, enabling cislunar activities, and implementing key technologies for missions to Mars. Autonomy is a capability area necessary to handle long communication outages where intervention from Earth is impossible, to prepare to operate with long communication delays that will be common in interplanetary travel, and to make spaceflight more affordable and accessible by reducing sustaining operations costs. The Gateway Concept of Operations states that one of Gateway’s goals is to “focus on infrastructure and systems that will allow autonomous operations aboard the Gateway with robotics, automated systems, advanced communications, and distributed computing.” Gateway’s Vehicle Systems Manager (VSM) and associated Autonomous Spacecraft Management Architecture (ASMA) are key products towards delivering autonomous capability. The primary functions of the control architecture are Mission Management and Timeline Execution, Resource Management, Fault Management, and Vehicle Control and Operation (VCO). In each of these areas, there is an initial level of capability to be delivered at launch, with plans to continue development and grow to greater capability. The initial deployment of VSM will focus on maintaining vehicle safety by focusing on full fault management capabilities and deploying only enough resource and timeline planning functionality to support that. The final deployment of VSM will add significant planning and control optimization functionality to support nominal operations for up to 21 days without ground support, even accommodating fault and failure conditions. While the VSM is the vehicle-level representation of autonomous reasoning, distributed automation is essential to provide the right scope and abstraction of information to process. Module and system support of automation and simplicity of interfaces are two important design paradigms that Gateway is focusing on to garner a systems approach to autonomy. Distribution of reasoning can increase complexity, so Gateway is also taking a strict hierarchical approach to information flow and decision making. VSM is not the only capability necessary to achieve an autonomous spacecraft. Robotics support for maintenance of the spacecraft will be essential to provide continued vehicle functionality even when crew is not present. Technical and programmatic challenges exist when implementing autonomous robotics operations. These challenges include sufficient network flexibility to support data transfer to the rest of the vehicle to coordinate module-to-module robotic walk-offs and finding the proper interfaces to allow sufficient dexterity. Communication system upgrades planned for Gateway include Delay Tolerant Networking to best utilize the complex network of relays that will be part of mature cislunar operations. Distributed computing and management will provide failure tolerance, robustness, and growth of capabilities while still allowing significant reuse of heritage software on heritage systems as well as reuse of common applications across a spacecraft to minimize new development, but this requires adherence to key standards and interfaces. The Gateway program has demonstrated significant progress towards these capabilities and has identified challenges other spacecraft developers should be aware of from the start.

Molly Anderson↗
NASA NTRSDate not supplied

Gateway Autonomy for Enabling Deep Space Exploration

The Gateway spacecraft is an important stepping-stone to exploration of the solar system, integrating commercial and international partners into a tightly coupled system, enabling cislunar activities, and implementing key technologies for missions to Mars. Autonomy is a capability area necessary to handle long communication outages where intervention from Earth is impossible, to prepare to operate with long communication delays that will be common in interplanetary travel, and to make spaceflight more affordable and accessible by reducing sustaining operations costs. The Gateway Concept of Operations states that one of Gateway’s goals is to “focus on infrastructure and systems that will allow autonomous operations aboard the Gateway with robotics, automated systems, advanced communications, and distributed computing.” Gateway’s Vehicle Systems Manager (VSM) and associated Autonomous Spacecraft Management Architecture (ASMA) are key products towards delivering autonomous capability. The primary functions of the control architecture are Mission Management and Timeline Execution, Resource Management, Fault Management, and Vehicle Control and Operation (VCO). In each of these areas, there is an initial level of capability to be delivered at launch, with plans to continue development and grow to greater capability. The initial deployment of VSM will focus on maintaining vehicle safety by focusing on full fault management capabilities and deploying only enough resource and timeline planning functionality to support that. The final deployment of VSM will add significant planning and control optimization functionality to support nominal operations for up to 21 days without ground support, even accommodating fault and failure conditions. While the VSM is the vehicle-level representation of autonomous reasoning, distributed automation is essential to provide the right scope and abstraction of information to process. Module and system support of automation and simplicity of interfaces are two important design paradigms that Gateway is focusing on to garner a systems approach to autonomy. Distribution of reasoning can increase complexity, so Gateway is also taking a strict hierarchical approach to information flow and decision making. VSM is not the only capability necessary to achieve an autonomous spacecraft. Robotics support for maintenance of the spacecraft will be essential to provide continued vehicle functionality even when crew is not present. Technical and programmatic challenges exist when implementing autonomous robotics operations. These challenges include sufficient network flexibility to support data transfer to the rest of the vehicle to coordinate module-to-module robotic walk-offs and finding the proper interfaces to allow sufficient dexterity. Communication system upgrades planned for Gateway include Delay Tolerant Networking to best utilize the complex network of relays that will be part of mature cislunar operations. Distributed computing and management will provide failure tolerance, robustness, and growth of capabilities while still allowing significant reuse of heritage software on heritage systems as well as reuse of common applications across a spacecraft to minimize new development, but this requires adherence to key standards and interfaces. The Gateway program has demonstrated significant progress towards these capabilities and has identified challenges other spacecraft developers should be aware of from the start.

Molly Anderson↗
NASA NTRSDate not supplied

Innovations in Distributed Spacecraft Autonomy

The Distributed Spacecraft Autonomy (DSA) team at NASA’s Ames Research Center is advancing autonomy in distributed space systems through five key technical areas: distributed resource and task management, reactive operations, system modeling and simulation, human-swarm interaction, and ad hoc network communications. This talk describes recent successes of the DSA experiment onboard the Starling 1.0 mission.

Caleb Adams↗
NASA NTRSDate not supplied

Model-Based Systems Engineering, Real-Time Operations, and Autonomy

Model-Based Systems Engineering has been enabled by the development of the SysML language and software tools to create systems models. Systems models described in SysML incorporate frames (Diagrams) that represent behaviors (activities, sequences, state machines, use cases), requirements, and structure (definitions, internal structure, parametric formulation, and packaging). The SysML models are, in turn, used by applications to do analysis and studies of the designs and operational capabilities. These uses of the model are based on simulations, and do not include hardware. This paper presents a software environment and processes that enables more comprehensive systems models for MBSE, and use of these rich models for real-time operations. The paper describes a software platform that enables creation of comprehensive models, beyond what is now possible with SysML and related software tools, called the NASA Platform for Autonomous Systems (NPAS). The platform encapsulates a paradigm and infrastructure for creating systems models with complexity levels comparable to the ones handled by SysML software tools, but with additional fidelity that includes detailed design diagrams encompassing sensors, components, and design topologies. Furthermore, NPAS enables incorporation of data, information, and knowledge (DIaK) to implement autonomy and Integrated System Health Management (ISHM) and the inherent integration of content encompassing SysML structure and behavior diagrams throughout the NPAS modelAnd lastly, the NPAS models are used in real-time operations, taking advantage of the fidelity and complexity encompassed in the models in order to implement “thinking” ISHM and/or autonomous operations. . Incorporation of SysML model content into an NPAS model is briefly discussed.

MBSE↗
NASA NTRS2002

Onboard autonomy on the Three Corner Sat mission

In 2003, the student-built three satellite constellation Three Corner Sat (3CS) Mission will demonstrate onboard autonomy including: science data validation and prioritization, mission re-planning, and robust execution. Future observations will be planned onboard based on the quality of aquired science, available memory and power, and anticipated downlinks. These capabilities will allow 3CS to aquire additional science data if resources are available and to return only the highest quality science data.

Planning scheduling autonomy↗
NASA NTRS2001

Mission data system and the future of autonomy at Jet Propulsion Laboratory

Planetary missions require significant levels of autonomy, due to the critical, one-time-only, nature of many mission objectives, and to the long delay times for human intervention. These missions also operate in highly uncertain environments, such as planetary atmospheres or surfaces. Mission Data Systems (MDS) is a project to construct a reusable software architecture for planetary missions, based on identifying the states of the mission.

MDS autonomy↗
NASA NTRS2006

Reduction of User Interaction by Autonomy

This paper describes experiments that quantify the improvement that autonomous behaviors enable in the amount of user interaction required to navigate a robot in urban environments. Many papers have discussed various ways to measure the absolute level of autonomy of a system; we measured the relative improvement of autonomous behaviors over teleoperation across multiple traverses of the same course. We performed four runs each on an 'easy' course and a 'hard' course, where half the runs were teleoperated and half used more autonomous behaviors. Statistics show 40-70% reductions in the amount of time the user interacts with the control station; however, with the behaviors tested, user attention remained on the control station even when he was not interacting. Reducing the need for attention will require better obstacle detection and avoidance and better absolute position estimation.

attention↗