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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1982

Implicit numerical integration for periodic solutions of autonomous nonlinear systems

A change of variables that stabilizes numerical computations for periodic solutions of autonomous systems is derived. Computation of the period is decoupled from the rest of the problem for conservative systems of any order and for any second-order system. Numerical results are included for a second-order conservative system under a suddenly applied constant load. Near the critical load for the system, a small increment in load amplitude results in a large increase in amplitude of the response.

Thurston, G. A.↗
NASA NTRSDate not supplied

The Troupe System: an Autonomous Multi-Agent Rover Swarm

Autonomous cooperative robotic systems are the future of space exploration. The complexity of such systems makes their development, verification and assurance challenging. The Robust Software Engineering group at NASA Ames has developed the Troupe project that aims to explore the design and development of a swarm of autonomous rovers tasked to perform autonomous exploration and mapping of an unknown terrain. In this paper, we showcase the system design, and accompanying verification and validation tools integrated in the Troupe system development life-cycle.

TROUPE↗
NASA NTRS2021

An Uncertainty Quantification Framework for Autonomous Flight System Tracking and Health Monitoring

This work proposes a perspective towards establishing a framework for uncertainty quantification of autonomous system tracking and health monitoring. The approach leverages the use of a predictive process structure, which maps uncertainty sources and their interaction according to the quantity of interest and the goal of the predictive estimation. It is systematic and uses basic elements that are system agnostic, and therefore needs to be tailored according to the specificity of the application. This work is motivated by the interest in low-altitude unmanned aerial vehicle operations, where awareness of vehicle and airspace state becomes more relevant as the density of autonomous operations grows rapidly. Predicted scenarios in the area of small vehicle operations and urban air mobility have no precedent, and holistic frameworks to perform prognostics and health management (PHM) at the system- and airspace-level are missing formal approaches to account for uncertainty. At the end of the paper, two case studies demonstrate implementation framework of trajectory tracking and health diagnosis for a small unmanned aerial vehicle. This work has been accepted for publication at the International Journal of Prognostics and Health Management Jan 2021. Minor edits have been incorporated to this original submission to incorporate complete overview and software integration.

Uncertainty Quantification↗
NASA NTRS2005

Towards an Autonomic Cluster Management System (ACMS) with Reflex Autonomicity

Cluster computing, whereby a large number of simple processors or nodes are combined together to apparently function as a single powerful computer, has emerged as a research area in its own right. The approach offers a relatively inexpensive means of providing a fault-tolerant environment and achieving significant computational capabilities for high-performance computing applications. However, the task of manually managing and configuring a cluster quickly becomes daunting as the cluster grows in size. Autonomic computing, with its vision to provide self-management, can potentially solve many of the problems inherent in cluster management. We describe the development of a prototype Autonomic Cluster Management System (ACMS) that exploits autonomic properties in automating cluster management and its evolution to include reflex reactions via pulse monitoring.

Truszkowski, Walt↗
NASA NTRSDate not supplied

Enabling a Voice Management System for Space Applications, Design and Software Development

Sustainable missions, beyond low Earth orbit, will require autonomous capabilities in order to achieve NASA’s Artemis program objectives. Correspondingly, the crew must have a means to efficiently interact with these autonomous systems; this can be facilitated via voice and speech communications. Voice-based controls enable the user to access autonomous systems hands-free/eyes-free, allowing the user to better focus on critical tasks. The goal of this project was to explore the knowledge and technology needed to successfully design effective voice interfaces for autonomous systems. The main objective was to understand how a crew member, through voice interaction, could most efficiently and intuitively communicate with a notional autonomous vehicle system manager. This project leveraged prior research conducted by the University of Michigan’s Bioastronautics and Life Support System (BLiSS) team as part of a NASA Moon to Mars eXploration Systems and Habitation (M2M X-Hab) 2020 Academic Innovation Challenge. The X-Hab 2020 work from the BliSS Team resulted in an intuitive graphical user interface/user experience that was built on an Internet of Things (IOT) platform. The Voice User Interface (VUI) design for the M2M X-Hab 2021 project leveraged this technology and incorporated a voice-based assistant and NASA’s Platform for Autonomous Systems (NPAS) software. This required technologies to convert voice to text, conduct semantic interpretations, and convert responses from the autonomous system to text and to speech; additionally, the background noise environment of spacecraft was assessed, and a relatable personality for the autonomous system to facilitate human-like conversations was created. This work’s success was largely due to the diverse team that included expertise in Space Systems Engineering, Human Computer Interaction, Aerospace Engineering, Computer Science, Biomedical Engineering, and Applied Physics. The differing perspectives fostered elaborate discussions, resulting in the conception of three main interactions: (1) User-System, (2) NPAS-System, and (3) Environment-System. The system developed, i.e. the VUI, had to be unique, efficient, and intuitive; thus, the team crafted a personality for the system to enable human-like conversation. User surveys sent to students and young professionals were used to help determine these personality traits by capturing perspectives and expectations of the “Artemis Generation Astronauts”. To further simulate human-like conversations, the system had to be able to quickly interpret user speech and be able to integrate with NASA’s NPAS system for quick and reliable information transfer. Results of this research include (1) a working prototype user interface, that is compatible with NASA’s NPAS system; (2) software that demonstrates the ability to interpret user requests and respond appropriately; (3) the capability to implement fully expanded conversations between user and system using intuitive communication in four request categories; and (4) software and hardware recommendations that optimize the system’s ability to operate, i.e. be heard, in a noisy environment. The technologies chosen for this project’s demonstrations included the following: Raspberry Pi, RASA, Mozilla Deep Speech, Coqui, RTX Voice and Adobe XD. This work has laid the foundation for the development of VUI’s used for autonomy, and is intended to provide guidance for future VUI development.

Tara Vega↗
NASA NTRS2015

Multi-Spacecraft Autonomous Positioning System

As the number of spacecraft in simultaneous operation continues to grow, there is an increased dependency on ground-based navigation support. The current baseline system for deep space navigation utilizes Earth-based radiometric tracking, requiring long-duration observations to perform orbit determination and generate a state update. The age, complexity, and high utilization of the ground assets pose a risk to spacecraft navigation performance. In order to perform complex operations at large distances from Earth, such as extraterrestrial landing and proximity operations, autonomous systems are required. With increasingly complex mission operations, the need for frequent and Earth-independent navigation capabilities is further reinforced. The Multi-spacecraft Autonomous Positioning System (MAPS) takes advantage of the growing interspacecraft communication network and infrastructure to allow for Earth-autonomous state measurements to enable network-based space navigation. A notional concept of operations is given in figure 1. This network is already being implemented and routinely used in Martian communications through the use of the Mars Reconnaissance Orbiter and Mars Odyssey spacecraft as relays for surface assets. The growth of this communications architecture is continued through MAVEN, and future potential commercial Mars telecom orbiters. This growing network provides an initial Marslocal capability for inter-spacecraft communication and navigation. These navigation updates are enabled by cross-communication between assets in the network, coupled with onboard navigation estimation routines to integrate packet travel time to generate ranging measurements. Inter-spacecraft communication allows for frequent state broadcasts and time updates from trusted references. The architecture is a software-based solution, enabling its implementation on a wide variety of current assets, with the operational constraints and measurement accuracy determined by onboard systems.

Anzalone, Evan↗
DOE OSTI2025

Fast and Scalable FFT-Based GPU-Accelerated Algorithms for Block-Triangular Toeplitz Matrices with Application to Linear Inverse Problems Governed by Autonomous Dynamical Systems

In this work, we present an efficient and scalable algorithm for performing matrix-vector multiplications (matvecs) for block Toeplitz matrices. Such matrices, which are shift-invariant with respect to their blocks, arise in the context of solving inverse problems governed by autonomous systems, and time-invariant systems in particular. In this article, we consider inverse problems that infer unknown parameters from observational data of a linear time-invariant dynamical system given in the form of partial differential equations (PDEs). Matrix-free Newton-conjugate-gradient methods are often the gold standard for solving these inverse problems, but they require numerous actions of the Hessian on a vector. Matrix-free adjoint-based Hessian matvecs require solution of a pair of linearized forward/adjoint PDE solves per Hessian action, which may be prohibitive for large-scale inverse problems. Time invariance of the forward PDE problem leads to a block Toeplitz structure of the discretized parameter-to-observable (p2o) map defining the mapping from inputs (parameters) to outputs (observables) of the PDEs. This block Toeplitz structure enables us to exploit two key properties: (1) compact storage of the p2o map and its adjoint, and (2) efficient fast Fourier transform–based Hessian matvecs. The proposed algorithm is mapped onto large multi-GPU clusters and achieves more than 80% of peak bandwidth on NVIDIA A100 GPUs. Excellent weak scaling is shown for up to 48 A100 GPUs. For the targeted problems, the implementation executes Hessian matvecs within fractions of a second, which is orders of magnitude faster than can be achieved by conventional matrix-free Hessian matvecs via forward/adjoint PDE solves.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Airspace Integration Considerations for Increasingly Autonomous Flight and Operations

There is much interest in autonomous systems and their operations. When it comes to autonomous systems, it is critical to understand various levels of proposed autonomy, reasons for selecting autonomous capabilities, certification approaches and various challenges or research needs associated with the integration of autonomous systems in the National Airspace System (NAS). Furthermore, there are levels of autonomous systems proposed for different types of aircraft including: single pilot operations, remotely piloted operations, fully autonomous operations, and multiple aircraft managed by a single, remote pilot. This preliminary article aims to provide a broad overview rather than concentrate on a specific element of autonomous flight. Hence, the focus of this article is on enabling various types of increasingly autonomous aircraft and their operations routinely within the next five to ten years, consistent with the FAA’s Info-Centric NAS (ICN) vision. Under ICN, a fully integrated information environment supports collaboration across diverse traffic management services and shifts decision-making to the most appropriate actor supported by automation. However, the roles and responsibilities of Air Traffic Services (ATS) personnel providing traditional services are likely to be very similar to today in that humans will still be responsible for majority of decision making. There is another NASA research effort beyond ICN is underway called Sky For All which is not covered in this article as it is in the planning stages.

autonomy↗
DOE OSTI2025

Autonomous Fueling System for Heavy-Duty Fuel Cell Electric Trucks

The motivation for this project stemmed from the challenges associated with rapidly refueling heavy-duty hydrogen fuel cell electric trucks (FCETs). Current manual refueling processes for fast refueling involve large, heavy equipment (e.g., hoses three times heavier than standard) and pose ergonomic risks and potential for equipment damage. The goal was to develop and test an autonomous fueling system to improve ergonomics, enhance safety, increase equipment durability through design improvements, and potentially speed up the fueling process. This project aimed to add to the understanding of autonomous systems in the context of heavy-duty hydrogen refueling, evaluating the technical effectiveness of potential concepts. A successfully developed system would benefit the public by facilitating the adoption of zero-emission heavy-duty transport, reducing reliance on manual labor for a physically demanding task, and potentially improving the safety and efficiency of hydrogen refueling infrastructure. The major accomplishment during the project's active period was the completion of the system-level architecture task. This involved establishing a detailed list of system requirements covering interfaces, environmental conditions, regulatory compliance, industry standards, safety, security, performance capabilities, and optional features. Five key use cases for the autonomous system were also identified. However, due to internal restructuring at Nikola, the necessary resources could not be allocated to continue the project. Consequently, Nikola opted to discontinue the project. The award was mutually terminated by Nikola and the DOE.

08 HYDROGEN↗
DOE OSTI2025

Development of a Multi-Robot System for Autonomous Inspection of Nuclear Waste Tank Pits

This paper introduces the overall design plan, development timeline, and preliminary progress of the Autonomous Pit Exploration System project. This project aims to develop an advanced multi-robot system for the efficient inspection of nuclear waste-storage tank pits. The project is structured into three phases: Phase 1 involves data collection and interface definition in collaboration with Hanford Site experts and university partners, focusing on tank riser geometry and hardware solutions. Phase 2 includes the selection of sensors and robot components, detailed mechanical design, and prototyping. Phase 3 integrates all components into a cohesive system managed by a master control package which also incorporates digital twin and surrogate models, and culminates in comprehensive testing and validation at a simulated tank pit at the Idaho National Laboratory. Additionally, the system’s communication design ensures coordinated operation through shared data, power, and control signals. For transportation and deployment, an electric vehicle (EV) is chosen to support the system for a full 10 h shift with better regulatory compliance for field deployment. A telescopic arm design is selected for its simple configuration and superior reach capability and controllability. Preliminary testing utilizes an educational robot to demonstrate the feasibility of splitting computational tasks between edge and cloud computers. Successful simultaneous localization and mapping (SLAM) tasks validate our distributed computing approach. More design considerations are also discussed, including radiation hardness assurance, SLAM performance, software transferability, and digital twinning strategies.

Nuclear waste management↗
NASA NTRS2018

Trusted Communication: Utilizing Speech Communication to Enhance Human-Machine Teaming Success

An area of increasing interest for the next generation of aircraft is autonomy and the integration of increasingly autonomous systems into the national airspace. Such an integration requires humans to work closely with autonomous systems, forming teams. Our hypothesis is that a team composed of both humans and autonomous systems will operate better than either entity alone. We have existing procedures for certifying pilots to operate in the national airspace and are currently working on methods for validating the function of autonomous systems, however we have no method in place for assessing the interaction of these two disparate systems. Communication is one avenue. This paper will examine the use of language as a metric for ascertaining human-machine teaming effectiveness. A proof-of-concept of the application of two communication-based analysis techniques, Linguistic Inquiry and Word Count (LIWC) and Latent Semantic Analysis (LSA), for the prediction of success in human/chatbot teaming was conducted. By running these analyses over data from the 2014 and 2015 Loebner Prize competitions of human/chatbot teaming, numerical scores were obtained that can be associated with scores provided by human judges during the competition. Correlating their LIWC and LSA data with the scores provided by the judges, and using linear regression over this correlation, formulae were obtained that predict the score of human/chatbot interaction. These formulae were tested over the 2013 Loebner Prize transcripts, determining that, though there was strong correlation between predicted and actual scores, the predictive success of this method was not strong. However, with specialized topic spaces and lexica, as well as larger data sets, the predictive power of these metrics will improve. Given the importance of providing metrics for human-machine system team success and given the promise shown by the communication-basedLIWCand LSAmethods, continuing research in this area is necessary. After examining the potential for using communication and spoken language as a metric for the success of human/autonomous system teaming, this paper then examines aspects inherent to communication systems that may contribute to unreliability and reduced trust. Modern natural language processing tools rely on deep learning algorithms to create language rules that produce accurate results, but these rules are uninterpretable. The resulting blackbox system lacks transparency necessary for full validation and complete trust. Additionally, speech-based interfaces pose other difficulties to developing coordinated teamwork between humans and autonomous systems. Human communication is infrequently limited to speech only, instead usually relying on a combination of verbal, gestural, and general body language communication. Reducing an analysis of team effectiveness to a study of spoken language alone is problematic as it leaves these other equally important forms of communication out. This paper will examine these problems and the general deficiencies in speech-based metrics for human-machine teaming.

E L Meszaros↗
NASA NTRS2012

Methods of determining complete sensor requirements for autonomous mobility

A method of determining complete sensor requirements for autonomous mobility of an autonomous system includes computing a time variation of each behavior of a set of behaviors of the autonomous system, determining mobility sensitivity to each behavior of the autonomous system, and computing a change in mobility based upon the mobility sensitivity to each behavior and the time variation of each behavior. The method further includes determining the complete sensor requirements of the autonomous system through analysis of the relative magnitude of the change in mobility, the mobility sensitivity to each behavior, and the time variation of each behavior, wherein the relative magnitude of the change in mobility, the mobility sensitivity to each behavior, and the time variation of each behavior are characteristic of the stability of the autonomous system.

Curtis, Steven A.↗
NASA NTRSDate not supplied

Risk-Reduction Autonomy Implementation to Enable NASA Artemis Missions

To achieve NASA’s Artemis program mission objectives a high level of autonomy and ubiquitous autonomy throughout the systems that are being developed will be necessary. The autonomous systems of Artemis will require a distributed autonomy capability, with autonomous systems organized functionally in a hierarchical architecture, where systems at higher levels of the hierarchy have authority over systems at lower levels. The challenge of developing autonomy technologies and concepts of operations for Artemis has been undertaken by the NASA Gateway Working Group. This group has developed requirements, architectures, concepts of operations, and interface control documents, in the context of a hierarchical distributed architecture that includes the following: a Vehicle System Manager (VSM) that autonomously manages the entire Gateway; Module System Managers (MSMs) that autonomously manage each module; and System Managers (SMs) that autonomously manage systems within a module(i.e. ECLSS).A substantially high level of autonomy needs to be achieved by each element of the hierarchy (VSM, MSM, SM)to meet requirements for uncrewed operations; this includes conditions that will have minimal and/or delayed ground intervention (i.e. requirements for sustainability for months of operation without crew or ground support). To advance an implementation of this autonomy design (Gateway Autonomy Design –GAD), a collaboration was established between the Autonomous Systems Laboratory (ASL) at NASA Stennis Space Center and Lockheed Martin. The objectives of this partnership were the following: (1)to implement autonomy at the VSM, MSM, and SM levels;(2) to implement communications among a VSM, 2 MSMs, ORION (a visiting vehicle somewhat equivalent to a module) and 1 SM (a power system), and (3) test autonomous operations with representative use cases. A SM backed by a high-fidelity simulation was created to facilitate demonstrations of use cases that originated in a system of a module. Communication between the VSM and MSMs was implemented according to Concepts of Operations and Interface Control Documents (ICDs). Demonstrations were conducted to address nominal and off-nominal operations and multi-module interactions with VSM. Additionally, user interfaces were created to provide awareness about ongoing processes and results while enhancing the demonstration. Demonstrations included the following use cases: (1)Orion as visiting vehicle registers with VSM;(2) VSM reschedules a module’s timelines when another module’s MSM task fails; and (3) a module’s Power System Manager (PSM) standalone demonstration that included component failure diagnostics, tracing component failure to effected components, which in turn, reports failure information up to the VSM for acknowledgement and display. This paper will describe the detailed technology and autonomous systems developed, and the integrated multi-module demonstrations conducted. Also, challenges that must be met to fully implement the GAD defined by Gateway will be addressed.

Autonomous Systems↗
NASA NTRS1990

Knowledge-based and integrated monitoring and diagnosis in autonomous power systems

A new technique of knowledge-based and integrated monitoring and diagnosis (KBIMD) to deal with abnormalities and incipient or potential failures in autonomous power systems is presented. The KBIMD conception is discussed as a new function of autonomous power system automation. Available diagnostic modelling, system structure, principles and strategies are suggested. In order to verify the feasibility of the KBIMD, a preliminary prototype expert system is designed to simulate the KBIMD function in a main electric network of the autonomous power system.

Momoh, J. A.↗
NASA NTRSDate not supplied

Cislunar Autonomous Positioning System Technology Operations and Navigation Experiment (CAPSTONE) Pathfinder for Artemis Gateway

The Cislunar Autonomous Positioning System Technology Operations and Navigation Experiment (CAPSTONE) mission was developed by NASA in collaboration with Advanced Space, LLC of Westminster, Colorado. This technology demonstration mission serves as a pathfinder for near rectilinear halo orbit (NHRO) operations around the Moon. The NHRO, (Perilune = 3,200 km; Apolune = 70,000 km) is the intended orbit for NASA’s Artemis Gateway, a small, human-tended space station planned for lunar orbit. The CAPSTONE mission will validate simulations and confirm operational planning for Gateway while also validating performance of navigation and stationkeeping requirements for Gateway’s Power and Propulsion Element. Therefore, this mission will provide operational experience to NASA, commercial, and international missions for operations in a demanding orbital regime. The CAPSTONE mission consists of a 12-unit (U)+ CubeSat developed, integrated, and tested by the Terran Orbital Corporation that carries a payload communications system capable of crosslink ranging with NASA’s Lunar Reconnaissance Orbiter (LRO). CAPSTONE contains a chip-scale atomic clock (CSAC) for a one-way ranging experiment with NASA’s Deep Space Network, a dedicated payload flight computer for software demonstration, and a camera. The launch, coordinated by NASA’s Launch Services Program, was provided by Rocket Lab on its Electron launch vehicle using their Photon upper stage to deploy the CAPSTONE spacecraft. The mission launched June 28, 2022. The CAPSTONE spacecraft deployed from the Photon stage and traversed an approximately 4-month, highly fuel-efficient transfer phase entering the NRHO November 13, 2022, for a six-month primary mission phase. The mission is currently in a twelve-month technology enhancement operations phase. The CAPSTONE technology demonstration mission is led by Advanced Space, LLC. Spacecraft development and mission operations are conducted by Terran Orbital Corporation of Irvine, California. Noted accomplishments for the CAPSTONE mission include demonstrating the accessibility of NHROs; validating key operational concepts in the NHRO environment; laying the foundation for commercial support of future lunar operations; and accelerating the availability of peer-to-peer navigation capabilities provided by the Cislunar Autonomous Positioning System (CAPS). The CAPSTONE mission is funded through NASA’s Small Spacecraft Technology (SST) program, which is one of several programs within NASA’s Space Technology Mission Directorate. The program is chartered to develop and demonstrate technologies to enhance and expand the capabilities of small spacecraft with a particular focus on enabling new mission architectures through the use of small spacecraft, expanding the reach of small spacecraft to new destinations, and augmenting future missions with supporting small spacecraft. The CAPSTONE mission launch was provided by NASA’s Exploration Systems Development Missions Directorate’s Advanced Exploration Systems Division. Coordination and acquisition of the launch was managed by NASA’s Launch Services Program. The CAPSTONE mission and project status will be presented.

Elwood Agasid↗
NASA NTRSDate not supplied

Cislunar Autonomous Positioning System Technology Operations and Navigation Experiment (CAPSTONE) Pathfinder for Artemis Gateway

The Cislunar Autonomous Positioning System Technology Operations and Navigation Experiment (CAPSTONE) mission was developed by NASA in collaboration with Advanced Space, LLC of Westminster, Colorado. This technology demonstration mission serves as a pathfinder for near rectilinear halo orbit (NHRO) operations around the Moon. The NHRO, (Perilune = 3,200 km; Apolune = 70,000 km) is the intended orbit for NASA’s Artemis Gateway, a small, human-tended space station planned for lunar orbit. The CAPSTONE mission will validate simulations and confirm operational planning for Gateway while also validating performance of navigation and station-keeping requirements for Gateway’s Power and Propulsion Element. Thus, this mission will provide operational experience to NASA, commercial, and international missions for operations in a demanding orbital regime. The CAPSTONE mission consists of a 12-unit (U) CubeSat developed, integrated, and tested by the Terran Orbital Corporation that carries a payload communications system capable of cross-link ranging with NASA’s Lunar Reconnaissance Orbiter (LRO). CAPSTONE contains a chip scale atomic clock (CSAC) for a one-way ranging experiment with NASA’s Deep Space Network, a dedicated payload flight computer for software demonstration, and a camera. The launch, coordinated by NASA’s Launch Services Program, was provided by Rocket Lab on its Electron launch vehicle utilizing their Photon upper stage to deploy the CAPSTONE spacecraft into lunar orbit. The mission launched June 28, 2022. The CAPSTONE spacecraft deployed from Rocket Lab’s Photon stage and traversed an approximately 4 month highly efficient transfer phase entering the NRHO Novermber 13, 2022 for a six-month primary mission phase. The mission is currently in a twelve-month technology enhancement operations phase. The CAPSTONE technology demonstration mission is lead by Advanced Space, LLC. Spacecraft development and mission operations is conducted by Terran Orbital Corporation of Irvine, California. Noted accomplishments for the CAPSTONE mission include demonstration of the accessibility of NHROs, validation of key operational concepts in the NHRO environment, laying the foundation for commercial support of future lunar operations, and accelerating the availability of peer-to-peer navigation capabilities provided by the Cislunar Autonomous Positioning System (CAPS). The CAPSTONE mission is funded through NASA's Small Spacecraft Technology (SST) program, which is one of several programs within NASA’s Space Technology Mission Directorate. The program is chartered to develop and demonstrate technologies to enhance and expand the capabilities of small spacecraft with a particular focus on enabling new mission architectures through the use of small spacecraft, expanding the reach of small spacecraft to new destinations, and augmenting future missions with supporting small spacecraft. The CAPSTONE mission launch was provided by NASA’s Exploration Systems Development Missions Directorate’s Advanced Exploration Systems Division. Coordination and acquisition of the launch was managed by NASA’s Launch Services Program. The CAPSTONE mission and project status will be presented.

Elwood Agasid↗
NASA NTRS2006

Integrated System for Autonomous Science

The New Millennium Program Space Technology 6 Project Autonomous Sciencecraft software implements an integrated system for autonomous planning and execution of scientific, engineering, and spacecraft-coordination actions. A prior version of this software was reported in "The TechSat 21 Autonomous Sciencecraft Experiment" (NPO-30784), NASA Tech Briefs, Vol. 28, No. 3 (March 2004), page 33. This software is now in continuous use aboard the Earth Orbiter 1 (EO-1) spacecraft mission and is being adapted for use in the Mars Odyssey and Mars Exploration Rovers missions. This software enables EO-1 to detect and respond to such events of scientific interest as volcanic activity, flooding, and freezing and thawing of water. It uses classification algorithms to analyze imagery onboard to detect changes, including events of scientific interest. Detection of such events triggers acquisition of follow-up imagery. The mission-planning component of the software develops a response plan that accounts for visibility of targets and operational constraints. The plan is then executed under control by a task-execution component of the software that is capable of responding to anomalies.

Chien, Steve↗
NASA NTRS2021

Integrated System for Autonomous and Adaptive Caretaking (ISAAC) Simulated Cargo Logistics Demo Video

This video shows a simulation of autonomous cargo logistics using ISAAC (Integrated System for Autonomous and Adaptive Caretaking). The video shows autonomous planning and execution of a cargo scenario in which the R2 robot moves a cargo bag from a stowed location to a transfer location in the US Lab on the International Space Station. The Astrobee robot then moves the bag from the transfer location to a new temporary stowage location in the Japanese Experiment Module (JEM).

ISAAC↗