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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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We investigate the automation of a Mars aerobraking vehicle that uses reaction wheels for attitude and angular momentum control during atmospheric flythrough.
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Robotic lighter-than-air vehicles, or aerobots, provide a strategic platform for the exploration of planets and moons with an atmosphere, such as Venus, Mars, Titan and the gas giants.
A system consisting of a set of estimators and autonomous behaviors has been developed which allows robust coring from a low-mass rover platform, while accommodating for moderate rover slip. A redundant set of sensors, including a force-torque sensor, visual odometry, and accelerometers are used to monitor discrete critical and operational modes, as well as to estimate continuous drill parameters during the coring process. A set of critical failure modes pertinent to shallow coring from a mobile platform is defined, and autonomous behaviors associated with each critical mode are used to maintain nominal coring conditions. Autonomous shallow coring is demonstrated from a low-mass rover using a rotary-percussive coring tool mounted on a 5 degree-of-freedom (DOF) arm. A new architecture of using an arm-stabilized, rotary percussive tool with the robotic arm used to provide the drill z-axis linear feed is validated. Particular attention to hole start using this architecture is addressed. An end-to-end coring sequence is demonstrated, where the rover autonomously detects and then recovers from a series of slip events that exceeded 9 cm total displacement.
This work presents a novel cooperative path planning for formation keeping robots traversing along a road with obstacles and possible narrow passages. A unique challenge in this problem is a requirement for spatial and temporal coordination between vehicles while ensuring collision and obstacle avoidance.
Sensory processing of visual, auditory, and other sensor information (e.g., visual imagery, LIDAR, RADAR) is conventionally based on "stovepiped," or isolated processing, with little interactions between modules. Biological systems, on the other hand, fuse multi-sensory information to identify nearby objects of interest more quickly, more efficiently, and with higher signal-to-noise ratios. Similarly, examples of the OpenSense technology disclosed herein use neurally inspired processing to identify and locate objects in a robot's environment. This enables the robot to navigate its environment more quickly and with lower computational and power requirements.
Sensory processing of visual, auditory, and other sensor information (e.g., visual imagery, LIDAR, RADAR) is conventionally based on "stovepiped," or isolated processing, with little interactions between modules. Biological systems, on the other hand, fuse multi-sensory information to identify nearby objects of interest more quickly, more efficiently, and with higher signal-to-noise ratios. Similarly, examples of the OpenSense technology disclosed herein use neurally inspired processing to identify and locate objects in a robot's environment. This enables the robot to navigate its environment more quickly and with lower computational and power requirements.
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Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
The Multiple Autonomous Discrete Event Controllers for Constellations (MADECC) project is an effort within the National Aeronautics and Space Administration Goddard Space Flight Center's (NASA/GSFC) Information Systems Division to develop autonomous positioning and attitude control for constellation satellites. It will be accomplished using traditional control theory and advanced coordination algorithms developed by the Johns Hopkins University Applied Physics Laboratory (JHU/APL). This capability will be demonstrated in the discrete event control test-bed located at JHU/APL. This project will be modeled for the Leonardo constellation mission, but is intended to be adaptable to any constellation mission. To develop a common software architecture. the controllers will only model very high-level responses. For instance, after determining that a maneuver must be made. the MADECC system will output B (Delta)V (velocity change) value. Lower level systems must then decide which thrusters to fire and for how long to achieve that (Delta)V.
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. Knowledge-based software provides a robust method of control for highly complex space-based power systems that conventional methods do not allow. The project consists of three elements: the Autonomous Power Expert System (APEX) for fault diagnosis and control, the Autonomous Intelligent Power Scheduler (AIPS) to determine system configuration, and power hardware (Brassboard) to simulate a space based power system. The operation of the Autonomous Power System as a whole is described and the responsibilities of the three elements - APEX, AIPS, and Brassboard - are characterized. A discussion of the methodologies used in each element is provided. Future plans are discussed for the growth of the Autonomous Power System.
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. Knowledge-based software provides a robust method of control for highly complex space-based power systems that conventional methods do not allow. The project consists of three elements: the Autonomous Power Expert System (APEX) for fault diagnosis and control, the Autonomous Intelligent Power Scheduler (AIPS) to determine system configuration, and power hardware (Brassboard) to simulate a space based power system. The operation of the Autonomous Power System as a whole is described and the responsibilities of the three elements - APEX, AIPS, and Brassboard - are characterized. A discussion of the methodologies used in each element is provided. Future plans are discussed for the growth of the Autonomous Power System.
This paper describes an autonomous control concept for pointing and articulation of science instruments on the Eos (Earth observing system) NASA/NOAA platforms intended to be operational by the late 1990s. Key features of this concept include advanced control adaptation and tuning strategies which provide performance robustness over a wide range of system uncertainties and mission time criticality. System identification-control modification paradigms are synthesized to form an adaptation continuum over this extended regime of autonomous operations.
The JPL Supervisory Telerobotics Laboratory (STELER) has developed a unique local-remote robot control architecture which enables management of intermittent bus latencies and communication delays such as those expected for ground-remote operation of Space Station robotic systems via the TDRSS communication platform. At the local site, the operator updates the work site world model using stereo video feedback and a model overlay/fitting algorithm which outputs the location and orientation of the object in free space. That information is relayed to the robot User Macro Interface (UMI) to enable programming of the robot control macros. The operator can then employ either manual teleoperation, shared control, or supervised autonomous control to manipulate the object under any degree of time-delay. The remote site performs the closed loop force/torque control, task monitoring, and reflex action. This paper describes the STELER local-remote robot control system, and further describes the near-term planned Space Station applications, along with potential far-term applications such as telescience, autonomous docking, and Lunar/Mars rovers.