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This paper describes the application of an automated planning and scheduling system to the NASA Earth Orbitin 1 (EO-1) missions. The planning system, ASPEN, is used to autonomously schedule the daily activites of the satellite.
Planning will be an essential part of future autonomous robots and integrated intelligent systems. This paper focuses on learning problem solving knowledge in planning systems. The system is based on a common representation for macros, abstractions, and cases. Therefore, it is able to exploit both classical and case based techniques. The general operators in a successful plan derivation would be assessed for their potential usefulness, and some stored. The feasibility of this approach was studied through the implementation of a learning system for abstraction. New macros are motivated by trying to improve the operatorset. One heuristic used to improve the operator set is generating operators with more general preconditions than existing ones. This heuristic leads naturally to abstraction hierarchies. This investigation showed promising results on the towers of Hanoi problem. The paper concludes by describing methods for learning other problem solving knowledge. This knowledge can be represented by allowing operators at different levels of abstraction in a refinement.
Beginning next century, several schemes for sending a planetary rover to the moon or Mars are being planned. As part of the development program, autonomous navigation technology is being studied to allow the rover the ability to move autonomously over a long range of unknown planetary surface. In the previous study, we ran the autonomous navigation experiment on an outdoor test terrain by using a rover test-bed that was controlled by a conventional sense-plan-act method. In some cases during the experiment, a problem occurred with the rover moving into untraversable areas. To improve this situation, a new control technique has been developed that gives the rover the ability of reacting to the outputs of the proximity sensors, a reaction behavior if you will. We have developed a new rover test-bed system on which an autonomous navigation experiment was performed using the newly developed control technique. In this outdoor experiment, the new control technique effectively produced the control command for the rover to avoid obstacles and be guided to the goal point safely.
NASA plans to land the first woman and next man on the Moon by 2025 through the initial Artemis missions. NASA and its international partners plan to establish a sustainable long-term presence on the lunar surface and build up infrastructure in the subsequent Artemis missions. The Lunar Surface Innovation Initiative (LSII), within NASA’s Space Technology Mission Directorate, aims to spur the creation of novel technologies needed for lunar surface exploration and accelerate the technology readiness of key systems and components. The primary thrust areas of LSII include sustainable power; dust mitigation; in-situ resource utilization; surface excavation, construction, and outfitting; and extreme access/extreme environments. The Moon-to-Mars Planetary Autonomous Construction Technology (MMPACT) projectwas initiated to address the lunar surface construction thrust area of LSII. The goal of the MMPACT project is to develop, deliver, and demonstrate on-demand capabilities to protect astronauts and create infrastructure on the lunar surface via construction of landing pads, habitats, shelters, roadways, berms and blast shields using lunar regolith-based materials. The MMPACT project is leveraging technology derived from NASA’s 3D Printed Mars Habitat Challenge along with contributions from other Government agencies, and multiple partners within industry and academia. The MMPACT project is comprised of three interrelated elements, construction hardware and process development; feedstock materials development; and microwave structure construction capabilities. These elements are working together to address the multiple challenges of infrastructure construction on the surface of the Moon including increased autonomy of operations, hardware operation and manufacturing under lunar environmental conditions, long-duration operation of mechanisms and parts, scale of construction activities, and material and construction requirements and standards. This presentation will summarize the status of development activities in each of the three elements, including testing of the various candidate materials, preliminary design concepts for future lunar infrastructure elements, and the vision for future technology demonstrations on the lunar surface. These demonstrations, targeting the mid-to-late 2020’s, are expected to enable landing pad construction and habitat construction resulting in commercial capabilities early in the next decade.
NASA plans to land the first woman and next man on the Moon by 2025 through the initial Artemis missions. NASA and its international partners plan to establish a sustainable long-term presence on the lunar surface and build up infrastructure in the subsequent Artemis missions. The Lunar Surface Innovation Initiative (LSII), within NASA’s Space Technology Mission Directorate, aims to spur the creation of novel technologies needed for lunar surface exploration and accelerate the technology readiness of key systems and components. The primary thrust areas of LSII include sustainable power; dust mitigation; in-situ resource utilization; surface excavation, construction, and outfitting; and extreme access/extreme environments. The Moon-to-Mars Planetary Autonomous Construction Technology (MMPACT) project was initiated to address the lunar surface construction thrust area of LSII. The goal of the MMPACT project is to develop, deliver, and demonstrate on-demand capabilities to protect astronauts and create infrastructure on the lunar surface via construction of landing pads, habitats, shelters, roadways, berms and blast shields using lunar regolith-based materials. The MMPACT project is leveraging technology derived from NASA’s 3D Printed Mars Habitat Challenge along with contributions from other Government agencies, and multiple partners within industry and academia. The MMPACT project is comprised of three interrelated elements, construction hardware and process development; feedstock materials development; and microwave structure construction capabilities. These elements are working together to address the multiple challenges of infrastructure construction on the surface of the Moon including increased autonomy of operations, hardware operation and manufacturing under lunar environmental conditions, long-duration operation of mechanisms and parts, scale of construction activities, and material and construction requirements and standards. This presentation will summarize the status of development activities in each of the three elements, including testing of the various candidate materials, preliminary design concepts for future lunar infrastructure elements, and the vision for future technology demonstrations on the lunar surface. These demonstrations, targeting the mid-to-late 2020’s, are expected to enable landing pad construction and habitat construction resulting in commercial capabilities early in the next decade.
This paper describes the application of an automated planning and scheduling system to the NASA Earth Orbiting 1 (EO-1) mission. The planning system, ASPEN, is used to autonomously schedule the daily activites of the satellite. The satellite and operations constraints are encoded within a software model used by the planner.
Task-level control refers to the integration and coordination of planning, perception, and real-time control to achieve given high-level goals. Autonomous mobile robots need task-level control to effectively achieve complex tasks in uncertain, dynamic environments. This paper describes the Task Control Architecture (TCA), an implemented system that provides commonly needed constructs for task-level control. Facilities provided by TCA include distributed communication, task decomposition and sequencing, resource management, monitoring and exception handling. TCA supports a design methodology in which robot systems are developed incrementally, starting first with deliberative plans that work in nominal situations, and then layering them with reactive behaviors that monitor plan execution and handle exceptions. To further support this approach, design and analysis tools are under development to provide ways of graphically viewing the system and validating its behavior.
We are evaluating Playbook for CASAS (Connected Autonomous Smart Aerospace Systems), a tool designed to aid first responders in disaster relief efforts. We are adapting an existing tool, Playbook, to support a future unmanned aircraft system (UAS) swarm demonstration. Playbook for CASAS will be used to plan, edit, and monitor simulated UAS swarms, and we are interested in evaluating the user experience of this prototype as well as developing recommendations for future UAS interfaces. Allocation of roles and responsibilities between human-automation systems is key to promoting productive cooperation between users and automation. Future interfaces, however, must allow for adaptive management of the swarm not a constant split in human-automation control. Our early research indicates that when a single pilot is controlling swarms of robotic agents, such as UAS or ground rovers, operators require a higher level, goal-based interface with usability at its core. Along with that high-level control, users can leverage sensors within the swarm to be notified when lower level actions must be taken by the pilot. First responders working in disaster relief efforts require a high level of situational awareness (SA) and precise control at key moments within a mission. This balance in operator workload paired with SA can lead to improved safety and mission outcomes. Our research below outlines leverage points as well as the balance between human involvement and autonomy in UAS interfaces.
Roving planetary surface exploration missions operate using a pre-programmed plan that can limit the capability to effectively detect unexpected changes in terrain properties, adjust locomotion or sampling strategies, and autonomously identify scientifically valuable observations and adjust exploration strategies. In particular, the inability to measure and react to unexpected changes in regolith properties can negatively impact mission operations. The Mars Exploration Rover Spirit and InSight lander both experienced challenges related to understanding the geotechnical properties of regolith. The advancements in legged robotic platforms hold potential to address these challenges through greater sensitivity to changing surface properties. The Legged Autonomous Surface Science In Analogue Environments (LASSIE) project explores how legged roving platforms can use the leg motors to measure geotechnical properties of crusted and icy surface regolith and utilize those measurements to autonomously update the science operations plans. These tests will be performed in martian and lunar analog environments and also within lab settings.
Supervised autonomous assembly (SAA) will create a paradigm shift in the planning and design of future persistent assets (PAs), both in near zero-g environments and on planetary surfaces. SAA refers to an autonomy approach that has the benefits of autonomous assembly as well as the benefits provided by a supervisor (operator) who is available to resolve unexpected situations. SAA provides both increased design freedom as well as reduced programmatic risk. SAA enables evolution of future PAs over decades as in-space operations transition from single purpose missions to creation of PAs, such as laboratories and experimental stations which more closely resembling terrestrial laboratories that can easily adapt and evolve to new missions leveraging repeated visits to the PA. The ability to evolve enables PAs to rapidly respond to changing objectives resulting from new questions as our understanding improves. A recently initiated National Aeronautics and Space Administration (NASA) project in the Space Technology Mission Directorate (STMD) Game Changing Development (GCD) Program called the Precision Assembled Space Structure (PASS), leverages the advantages of SAA to develop technologies that enable efficient creation and evolution of hexagonal topologies; both planar (example: fuel depots) and curved (examples: telescopes and shelters). PASS will be used to provide context for the philosophy and concepts discussed as well as the decision and selections made. PASS objectives are: a) Develop confidence in SAA and on-orbit servicing, assembly and manufacturing (OSAM) technologies by executing a test campaign that uses a path-to-flight autonomous precision assembly process directly applicable to future space telescopes. b) Test autonomous technologies including automated path planning and error recovery, to emphasize a robust approach that relies on generic robots and special purpose tools. c) Validate critical component models using a digital twin that includes the assembled primary mirror support structure and assembly process. A digital twin is a high-fidelity simulation of the asset capable of predicting the on-orbit performance. The paper concludes after identifying the critical need for a modest assembly flight experiment to validate and develop confidence in the SAA paradigm, thus accelerating adoption of the benefits described. SAA is a game changing paradigm that enhances the ability of an organization to infuse new technology through rapid evolution of PAs while leveraging OSAM technologies.
N: The planning and execution of comprehensive “Prolonged Care” strategies are increasingly considered as fundamental operational concepts within the spheres of both military and non-military expeditionary medicine. Numerous advances in recent years have enabled the decentralization of health-related technology that has empowered medical - and even non-medical providers - to offer quality care farther afield and for increasingly extended periods of separation from traditional support structures. However, the operational concepts behind today’s Prolonged Care paradigms are still largely dependent upon external support, including high-bandwidth telemedicine communications technologies (that allow ready access to centralized expertise), robust resupply pathways, and ultimately, on the timely evacuation of the sick and injured. What would happen if none of these support systems were readily available? Extremely remote Prolonged Care is one of the most significant challenges that the National Aeronautics and Space Administration (NASA) faces when preparing to expand human space exploration beyond the International Space Station (ISS) to deep space including the Moon and Mars. Continuous human presence on the International Space Station (ISS) in low Earth orbit for the past 20 years has demonstrated that indeed medical operations can be successfully executed with continuous real-time communications, frequent resupply missions, and the availability of rapid evacuation options. However, as time-distance factors from Earth increase with our pursuit and changing focus towards deep space missions, these resources and capabilities will no longer be available. As a result, space medicine operations will need to become more autonomous and less dependent upon mission support from Earth – requiring our attention and efforts to build robust capabilities that support the ultimate expression of the term “Prolonged Field Care.”
The Space Automation and Robotics Center (SpARC), a NASA-sponsored Center for the Commercial Development of Space (CCDS), in conjunction with its corporate affiliates, is planning an on-orbit validation of autonomous rendezvous and docking (ARD) technology. The emphasis in this program is to utilize existing technology and commercially available components whenever possible. The primary subsystems that will be validated by this demonstration include GPS receivers for navigation, a video-based sensor for proximity operations, a fluid connector mechanism to demonstrate fluid resupply capability, and a compliant, single-point docking mechanism. The focus for this initial experiment will be expendable launch vehicle (ELV) based and will make use of two residual Commercial Experiment Transporter (COMET) service modules. The first COMET spacecraft will be launched in late 1992 and will serve as the target vehicle. The ARD demonstration will take place in late 1994, after the second COMET spacecraft has been launched. The service module from the second COMET will serve as the chase vehicle.
Celestial navigation (CelNav) is a source of navigation observables where images of known solar system bodies are used to locate a spacecraft, beneficial within the solar system for both cislunar and deep space missions. CelNav provides a variety of design benefits to support and enable current and new autonomous space operations- using only a camera and a processor to produce in-situ measurements for navigation. This technology reduces subscription to ground-based tracking during all phases of a mission, freeing up resources for other operational needs. This also supports secure navigation since it eliminates the need for ground contact. CelNav enables missions where the light time delay between Earth and the spacecraft is too long (or the Earth to spacecraft line of sight is obscured) to support critical operations. It also enables smaller mission classes, where Deep Space Network (DSN)time is cost prohibitive, to reduce its cost by focusing primarily on data downlink. Finally, it enables the NASA Artemis program and other cislunar human space flight by providing redundant navigation to traditional radiometric tracking. In this presentation, we discuss the implementation of a CelNav app in autonomous Navigation, Guidance, and Control (autoNGC), a comprehensive flight software suite for onboard autonomy that is built on the core Flight System (cFS). The presentation also summarizes the results of flight software-in-the-loop (SIL) and processor-in-the-loop (PIL) demonstrations. Both are high-fidelity simulations with the use of a camera emulator hosted on a GPU server that simulates images that would be captured by the camera. The CelNav app leverages the use of cGIANT (cFS Goddard Image Analysis and Navigation Tool).Previously developed for the autoNGC software suite, cGIANT is an onboard autonomous image processing and optical navigation (OpNav) tool that performs limb-based OpNav and Terrain Relative Navigation. The added CelNav capability of cGIANT generates bearing measurements to multiple known celestial bodies (planets, moons, asteroids, comets, etc.) in monocular (2D) images. These observables are then fed to the Goddard Enhanced Onboard Navigation System (GEONS)navigation filter app, enabling us to navigate the spacecraft autonomously. In early 2025, the autoNGC CelNav capability is planned to be flight tested as part of the onboard autonomy experiment on the Cislunar Autonomous Positioning System Technology Operations and Navigation Experiment(CAPSTONE) spacecraft that is currently in a Lunar Near Rectilinear Halo Orbit(NRHO).
The Autonomous Power System (APS) project at NASA Lewis Research Center is designed to demonstrate the abilities of integrated intelligent diagnosis, control and scheduling techniques to space power distribution hardware. The project consists of three elements: the Autonomous Power Expert System (APEX) for fault diagnosis, isolation, and recovery (FDIR), the Autonomous Intelligent Power Scheduler (AIPS) to determine system configuration, and power hardware (Brassboard) to simulate a space-based power system. Faults can be introduced into the Brassboard and in turn, be diagnosed and corrected by APEX and AIPS. The Autonomous Intelligent Power Scheduler controls the execution of loads attached to the Brassboard. Each load must be executed in a manner that efficiently utilizes available power and satisfies all load, resource, and temporal constraints. In the case of a fault situation on the Brassboard, AIPS dynamically modifies the existing schedule in order to resume efficient operation conditions. A database is kept of the power demand, temporal modifiers, priority of each load, and the power level of each source. AIPS uses a set of heuristic rules to assign start times and resources to each load based on load and resource constraints. A simple improvement engine based upon these heuristics is also available to improve the schedule efficiency. This paper describes the operation of the Autonomous Intelligent Power Scheduler as a single entity, as well as its integration with APEX and the Brassboard. Future plans are discussed for the growth of the Autonomous Intelligent Power Scheduler.
Usability encompasses learnability, efficiency, memorability, effectiveness, and satisfaction. NASA’s standards for usability acceptance criteria focus on interfaces that help operators achieve their tasks efficiently, effectively, and with satisfaction. However, discussions on usability, especially regarding future highly automated and autonomous systems, rarely include trust. As NASA plans for long-duration exploration missions, it envisions astronauts operating more independently from Mission Control on Earth. This independence will drive the development of these highly automated and autonomous systems that astronauts will use daily. To prepare for this future, our team has developed a scheduling and execution software tool that facilitates self-scheduling, allowing astronauts to independently manage their own schedule without Mission Control’s involvement. Over many years, we have developed, matured, and evaluated our software tool in extreme environments, prioritizing user-centered design and high usability. These evaluations have included multiple campaigns in NASA analogs, including NEEMO, BASALT, and HERA, as well as technology demonstrations onboard the International Space Station. Our recent research on software interfaces for future astronaut autonomy revealed a strong correlation between usability and trust measures. In a controlled lab experiment, we asked novice users to perform a complex scheduling task, during which the software immediately validated the schedule’s constraints and checked for violations. We collected usability (User Experience Questionnaire, UEQ) and trust (Trust in Automated Systems scale, TAS) measures; significant, strong, and moderate correlations emerged between several of the UEQ metrics and TAS. These results support the argument for investing in usability early to enable and sustain trust in highly automated and autonomous systems.
NASA Platform for Autonomous Systems (NPAS) is a disruptive software platform and processes being developed by SSC Autonomous Systems Laboratory (ASL). Autonomous operations are critical for the success, safety and crew survival of NASA deep space missions beyond low Earth orbit, including Lunar Orbital Platform-Gateway, and for the future of cost-effective ground mission operations. NPAS represents the embodiment of an innovative implementation for "thinking" autonomy in contrast to brute-force autonomy. It also uniquely addresses the requirements and integrates five primary functionalities for autonomous operations including: (1) Integrated System Health Management (ISHM), (2) autonomy, guided by health and system concepts of operations; (3) knowledge models of applications; (4) infrastructure to create, schedule, and execute mission plans; and (5) infrastructure to integrate distributed autonomous applications across networks.
This paper describes an integrated system for coordinating multiple rover behavior with the overall goal of collecting planetary surface data. The Multi-Rover Integrated Science Understanding System (MISUS) combines techniques from planning and scheduling with machine learning to perform autonomous scientific exploration with cooperating rovers.
This presentation is intended to show to many future stakeholders to explain the work NASA has planned under the System-Wide Safety project's Complex Autonomous Systems Assurance technical challenge.