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

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NASA NTRSDate not supplied

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

Future long duration exploration missions (LDEMs) bring new challenges to astronaut crews in deep space, one of which is increased communication latencies with ground stations. As a result, crews will have to behave more autonomously by self-scheduling their own operational timelines in an efficient and effective manner. To support crew autonomy, our team has spent the last few years developing Playbook, a mission planning and scheduling tool. Our research focuses on investigating scheduling performance using Playbook to inform the deployment of novel aids that streamline the timeline creation process and proposing relevant standards and guidelines for autonomous crews in LDEMs. Summarizing yearly progress of research analysis and experiment in HERA.

user experience↗
NASA NTRS1990

Situationally Driven Local Navigation for Mobile Robots

For mobile robots to autonomously accommodate dynamically changing navigation tasks in a goal-directed fashion, they must employ navigation plans. Any such plan must provide for the robot's immediate and continuous need for guidance while remaining highly flexible in order to avoid costly computation each time the robot's perception of the world changes. Due to the world's uncertainties, creation and maintenance of navigation plans cannot involve arbitrarily complex processes, as the robot's perception of the world will be in constant flux, requiring modifications to be made quickly if they are to be of any use. This work introduces navigation templates (NaT's) which are building blocks for the construction and maintenance of rough navigation plans which capture the relationship that objects in the world have to the current navigation task. By encoding only the critical relationship between the objects in the world and the navigation task, a NaT-based navigation plan is highly flexible; allowing new constraints to be quickly incorporated into the plan and existing constraints to be updated or deleted from the plan. To satisfy the robot's need for immediate local guidance, the NaT's forming the current navigation plan are passed to a transformation function. The transformation function analyzes the plan with respect to the robot's current location to quickly determine (a few times a second) the locally preferred direction of travel. This dissertation presents NaT's and the transformation function as well as the needed support systems to demonstrate the usefulness of the technique for controlling the actions of a mobile robot operating in an uncertain world.

Slack, Marc Glenn↗
NASA NTRS1992

Continuous motion using task-directed stereo vision

The performance of autonomous mobile robots performing complex navigation tasks can be dramatically improved by directing expensive sensing and planning in service of the task. The task-direction algorithms can be quite simple. In this paper we describe a simple task-directed vision system which has been implemented on a real outdoor robot which navigates using stereo vision. While the performance of this particular robot was improved by task-directed vision, the performance of task-directed vision in general is influenced in complex ways by many factors. We briefly discuss some of these, and present some initial simulated results.

Gat, Erann↗
NASA NTRS2018

Robonaut 2 and Watson: Cognitive Dexterity for Future Exploration

Future exploration missions will dictate a level of autonomy never before experienced in human spaceflight. Mission plans involving the uncrewed phases of complex human spacecraft in deep space will require a coordinated autonomous capability to be able to maintain the spacecraft when ground control is not available. One promising direction involves embedding intelligence into the system design both through the employment of state-of-the-art system engineering principles as well as through the creation of a cognitive network between a smart spacecraft or habitat and embodiments of cognitive agents. The work described here details efforts to integrate IBM's Watson and other cognitive computing services into NASA Johnson Space Center (JSC)'s Robonaut 2 (R2) anthropomorphic robot. This paper also discusses future directions this work will take. A cognitive spacecraft management system that is able to seamlessly collect data from subsystems, determine corrective actions, and provide commands to enable those actions is the end goal. These commands could be to embedded spacecraft systems or to a set of robotic assets that are tied into the cognitive system. An exciting collaboration with Woodside provides a promising Earth-bound testing analog, as controlling and maintaining not normally manned off-shore platforms have similar constraints to the space missions described.

Badger, Julia M.↗
NASA NTRS2014

Exploration Technologies for Operations

Although the International Space Station (ISS) assembly has been completed, the Operations support teams continue to seek more efficient and effective ways to prepare for and conduct the ISS operations and future exploration missions beyond low earth orbit. This search for improvement has led to a significant collaboration between the NASA research and advanced software development community at NASA Ames Research Center and the Mission Operations community at NASA Johnson Space Center. Since 2001, NASA Ames Research Center has been developing and applying its advanced intelligent systems and human systems integration research to mission operations tools for several of the unmanned Mars missions operations. Since 2006, NASA Ames Research Center has also been developing and applying its advanced intelligent systems and human systems integration research to mission operations tools for manned operations support with the Mission Operations Directorate at NASA Johnson Space Center. This paper discusses the completion of the development and deployment of a variety of intelligent and human systems technologies adopted for manned mission operations. The technologies associated with the projects include advanced software systems for operations and human-centered computing. Human-centered computing looks to the processes and procedures that people do to perform any given job, then attempts to identify opportunities to improve these processes and procedures. In particular, for mission operations, improvements are quantified by specifically identifying how a tool can increase a persons efficiency, enhance a persons functional capability, andor improve the assurance of a persons decisions. The Ames development team has collaborated with the Mission Operations team to identify areas of efficiencies through technology infusion applications in support of the Plan, Train, and Fly activities of human-spaceflight mission operations. The specific applications discussed in this paper are in the areas of mission planning systems, mission operations design modeling and workflow automation, advanced systems monitoring, mission control technologies, search tools, training management tools, spacecraft solar array management, spacecraft power management, and spacecraft attitude planning. We discuss these specific projects between the Ames Research Center and the Johnson Space Centers Mission Operations Directorate, and how these technologies and projects are enhancing the mission operations support for the International Space Station. We also discuss the challenges, problems, and successes associated with long-distance and multi-year development projects between the research team at Ames and the Mission Operations customers at Johnson Space center. Finally, we discuss how these technology infusion applications and underlying technologies might be used in the future to support on-board operations of the crew and spacecraft systems as human exploration expands beyond low earth orbit to destinations in the solar system where communications delays will require more on-board autonomy and planning by the crew. Longer communications delays will require that the ground mission operations support will be primarily strategic in nature, while the tactical level of planning, systems monitoring and control, and failure analysisisolationrecovery will be the responsibility of both the spacecraft autonomous systems and the crew. Our expectation is that the technologies

mission operations↗
NASA NTRS2019

Lessons Learned from International Space Station Crew Autonomous Scheduling Test

In 2017, our team investigated and evaluated the novel concept of operations of astronaut self-scheduling (rescheduling their own timeline without creating violations) onboard International Space Station (ISS). Five test sessions were completed for this technology demonstration called Crew Autonomous Scheduling Test (CAST). For the first time in a spaceflight operational environment, an ISS crewmember planned, rescheduled, and executed their activities in real-time on a mobile device while abiding by flight and scheduling constraints. This paper discusses the lessons learned from deployment to execution.

planning and scheduling↗
NASA NTRSDate not supplied

AEGIS: Autonomous Entity Global Intelligence System for Urban Air Mobility

This paper presents a global intelligence system that synthesizes aerial vehicles’ real-time physical data, planned actions, and historical behavior into engineered data frames representing the collective state of the airspace and suitable for efficient machine learning consumption. These data frames are then learnt by a deep neural net to build a prediction model that estimates the expected evolution path of the current state, thereby identifying potential future conflicts. This approach lends itself to an automated early warning system that the aerial vehicles can implement onboard with a suitable edge computing module more efficiently and effectively than non-AI methods, and eventually take preventive or corrective measures towards self/collaborative resolution of the issues. Contrary to a centralized early warning system where all vehicles’ task-space eventually converges to a global optimum state, the presented distributed global intelligence system brings in a balance between local utility functions of each vehicle and the global operating framework. This contributes to effectively handle the potential massive scaling in urban air mobility in the near future.

Artificial Intelligence↗
NASA NTRS2020

In-Time UAV Flight-Trajectory Estimation and Tracking Using Bayesian Filters

Rapid increase of UAV operation in the next decade in areas of on-demand delivery, medical transportation services, law enforcement, traffic surveillance and several others pose potential risks to the low altitude airspace above densely populated areas. Safety assessment of airspace demands the need for a novel UAV traffic management (UTM) framework for regulation and tracking of the vehicles. Particularly for low-altitude UAV operations, quality of GPS measurements feeding into the UAV is often compromised by loss of communication link caused by presence of trees or tall buildings in proximity to the UAV flight path. Inaccurate GPS locations may yield to unreliable monitoring and inaccurate prognosis of remaining battery life and other safety metrics which rely on future expected trajectory of the UAV. This work therefore proposes a generalized monitoring and prediction methodology for autonomous UAVs using in-time GPS measurements. Firstly, a typical 4D smooth trajectory generation technique from a series of waypoint locations with associated expected times-of-arrival based on B-spline curves is presented. Initial uncertainty in the vehicle's expected cruise velocity is quantified to compute confidence intervals along the entire flight trajectory using error interval propagation approach. Further, the generated planned trajectory is considered as the prior knowledge which is updated during its flight with incoming GPS measurements in order to estimate its current location and corresponding kinematic profiles. Estimation of position is denoted in dicrete state-space representation such that position at a future time step is derived from position and velocity at current time step and expected velocity at the future time step. A linear Bayesian filtering algorithm is employed to efficiently refine position estimation from noisy GPS measurements and update the confidence intervals. Further, a dynamic re-planning strategy is implemented to incorporate unexpected detour or delay scenarios. Finally, critical challenges related to uncertainty quantification in trajectory prognosis for autonomous vehicles are identified, and potential solutions are discussed at the end of the paper. The entire monitoring framework is demonstrated on real UAV flight experiments conducted at the NASA Langley Research Center.

Banerjee, Portia↗
NASA NTRSDate not supplied

Logistics Reduction Advancements and Future Plans for NASA's Exploration Missions

Management of logistics on exploration missions includes both looking for ways to minimize the quantities, mass and volume of various consumables, supplies, spares, and equipment as well as ways to minimize the crew time needed for locating and handling those items. Also included are ways to minimize the waste, handling, and resultant products from the processes of maintaining a crew on these missions. The Logistics Reduction project encompasses technologies for management of waste, trash, autonomous logistics, and clothing. This paper provides a status of work from 2023 in these areas including recent accomplishments and challenges encountered. Future objectives and plans for 2024 will also be covered along with the work currently in progress. Specifically, the paper will cover technologies in waste management, namely, the Universal Waste Management System (UWMS) or exploration toilet and work on an alternative waste collection container, the Alternate Fecal Canister. Trash management technologies work on the Trash Compaction Processing System (TCPS) and Trash to Gas (TtG) is summarized with progress to date as well as information on how Jettison as an option is related. Progress and summary of recent accomplishment on the RFID (Radio Frequency ID) Enabled Autonomous Logistics Management (REALM) and the Autonomous Logistics (AL) technologies is detailed. Advanced Clothing System (ACS) and work in the area of Systems Engineering and Integration (SE&I) is also included. Status of the technologies, accomplishments and how the focus areas inform program decisions will be addressed.

Toilet↗
NASA NTRSDate not supplied

Logistics Reduction Advancements and Future Plans for NASA's Exploration Missions

Management of logistics on exploration missions includes both looking for ways to minimize the quantities, mass and volume of various consumables, supplies, spares, and equipment as well as ways to minimize the crew time needed for locating and handling those items. Also included are ways to minimize the waste, handling, and resultant products from the processes of maintaining a crew on these missions. The Logistics Reduction project encompasses technologies for management of waste, trash, autonomous logistics, and clothing. This paper provides a status of work from 2023 in these areas including recent accomplishments and challenges encountered. Future objectives and plans for 2024 will also be covered along with the work currently in progress. Specifically, the paper will cover technologies in waste management, namely, the Universal Waste Management System (UWMS) or exploration toilet and work on an alternative waste collection container, the Alternate Fecal Canister. Trash management technologies work on the Trash Compaction Processing System (TCPS) and Trash to Gas (TtG) is summarized with progress to date as well as information on how Jettison as an option is related. Progress and summary of recent accomplishment on the RFID (Radio Frequency ID) Enabled Autonomous Logistics Management (REALM) and the Autonomous Logistics (AL) technologies is detailed. Advanced Clothing System (ACS) and work in the area of Systems Engineering and Integration (SE&I) is also included. Status of the technologies, accomplishments and how the focus areas inform program decisions will be addressed.

Toilet↗
NASA NTRS2024

Logistics Reduction Advancements and Future Plans for NASA's Exploration Missions

Management of logistics on exploration missions includes both looking for ways to minimize the quantities, mass and volume of various consumables, supplies, spares, and equipment as well as ways to minimize the crew time needed for locating and handling those items. Also included are ways to minimize the waste, handling, and resultant products from the processes of maintaining a crew on these missions. The Logistics Reduction project encompasses technologies for management of waste, trash, autonomous logistics, and clothing. This paper provides a status of work from 2023 in these areas including recent accomplishments and challenges encountered. Future objectives and plans for 2024 will also be covered along with the work currently in progress. Specifically, the paper will cover technologies in waste management, namely, the Universal Waste Management System (UWMS) or exploration toilet and work on an alternative waste collection container, the Alternate Fecal Canister. Trash management technologies work on the Trash Compaction Processing System (TCPS) and Trash to Gas (TtG) is summarized with progress to date as well as information on how Jettison as an option is related. Progress and summary of recent accomplishment on the RFID (Radio Frequency ID) Enabled Autonomous Logistics Management (REALM) and the Autonomous Logistics (AL) technologies is detailed. Advanced Clothing System (ACS) and work in the area of Systems Engineering and Integration (SE&I) is also included. Status of the technologies, accomplishments and how the focus areas inform program decisions will be addressed.

Toilet↗
NASA NTRS2016

Intelligent (Autonomous) Power Controller Development for Human Deep Space Exploration

As NASAs Evolvable Mars Campaign and other exploration initiatives continue to mature they have identified the need for more autonomous operations of the power system. For current human space operations such as the International Space Station, the paradigm is to perform the planning, operation and fault diagnosis from the ground. However, the dual problems of communication lag as well as limited communication bandwidth beyond GEO synchronous orbit, underscore the need to change the operation methodology for human operation in deep space. To address this need, for the past several years the Glenn Research Center has had an effort to develop an autonomous power controller for human deep space vehicles. This presentation discusses the present roadmap for deep space exploration along with a description of conceptual power system architecture for exploration modules. It then contrasts the present ground centric control and management architecture with limited autonomy on-board the spacecraft with an advanced autonomous power control system that features ground based monitoring with a spacecraft mission manager with autonomous control of all core systems, including power. It then presents a functional breakdown of the autonomous power control system and examines its operation in both normal and fault modes. Finally, it discusses progress made in the development of a real-time power system model and how it is being used to evaluate the performance of the controller and well as using it for verification of the overall operation.

Power Systems↗
NASA NTRS2012

POSTMAN: Point of Sail Tacking for Maritime Autonomous Navigation

Waves apply significant forces to small boats, in particular when such vessels are moving at a high speed in severe sea conditions. In addition, small high-speed boats run the risk of diving with the bow into the next wave crest during operations in the wavelengths and wave speeds that are typical for shallow water. In order to mitigate the issues of autonomous navigation in rough water, a hybrid controller called POSTMAN combines the concept of POS (point of sail) tack planning from the sailing domain with a standard PID (proportional-integral-derivative) controller that implements reliable target reaching for the motorized small boat control task. This is an embedded, adaptive software controller that uses look-ahead sensing in a closed loop method to perform path planning for safer navigation in rough waters. State-of-the-art controllers for small boats are based on complex models of the vessel's kinematics and dynamics. They enable the vessel to follow preplanned paths accurately and can theoretically control all of the small boat s six degrees of freedom. However, the problems of bow diving and other undesirable incidents are not addressed, and it is questionable if a six-DOF controller with basically a single actuator is possible at all. POSTMAN builds an adaptive capability into the controller based on sensed wave characteristics. This software will bring a muchneeded capability to unmanned small boats moving at high speeds. Previously, this class of boat was limited to wave heights of less than one meter in the sea states in which it could operate. POSTMAN is a major advance in autonomous safety for small maritime craft.

Huntsberger, Terrance L.↗
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 NTRS1987

Telerobot task planning and reasoning: Introduction to JPL artificial intelligence research

A view of the capabilities and areas of artificial intelligence research which are required for autonomous space telerobotics extending through the year 2000 is given. In the coming years, JPL will be conducting directed research to achieve these capabilities, as well as drawing heavily on collaborative efforts conducted with other research laboratories.

Atkinson, D. J.↗
NASA NTRSDate not supplied

An Experimental System for Strategic Flight Path Management in Advanced Air Mobility

In the concept envisioned for Urban Air Mobility (UAM) operations, fleets of electric vertical takeoff and landing (eVTOL) vehicles would operate between vertiports distributed within a densely populated area. These operations would be largely independent from the existing air traffic control system and would place the responsibility for flight planning and aircraft separation on fleet operators. The fourth major level on the UAM Maturity Level scale, UML-4, relies on “collaborative and responsible” automation to enable operations in non-visual conditions with medium traffic density (hundreds of aircraft in one metropolitan region) and medium complexity. This level of service places many requirements on automation systems to assist the operators of these aircraft. NASA has developed the Autonomous Operations Planner (AOP), a reference prototype Flight Path Management automation system, and has modified AOP to support research of anticipated UML-4 operations. AOP creates a four-dimensional flight plan conforming to the constraints of these operations, evaluates and modifies the flight plan during flight as conditions and constraints evolve, and coordinates the flight plan with other airspace users and with service providers. This version of AOP has been integrated into the Sikorsky Autonomy Research Aircraft and used in a flight test activity. In this paper we discuss anticipated characteristics of UAM operations, modifications that were made to AOP to adapt to that environment or to support the flight test, and observations of software and aircraft performance during the flight test. The aircraft achieved four-dimensional conformance with the flight plan and AOP provided adequate planning in almost all cases. We discuss improvements that could be made to AOP to address deficiencies that were observed.

Autonomous Operations Planner↗