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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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At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Supervised Remote Robot with Guided Autonomy and Teleoperation (SURROGATE): A Framework for Whole-Body Manipulation

The use of the cognitive capabilties of humans to help guide the autonomy of robotics platforms in what is typically called "supervised-autonomy" is becoming more commonplace in robotics research. The work discussed in this paper presents an approach to a human-in-the-loop mode of robot operation that integrates high level human cognition and commanding with the intelligence and processing power of autonomous systems. Our framework for a "Supervised Remote Robot with Guided Autonomy and Teleoperation" (SURROGATE) is demonstrated on a robotic platform consisting of a pan-tilt perception head, two 7-DOF arms connected by a single 7-DOF torso, mounted on a tracked-wheel base. We present an architecture that allows high-level supervisory commands and intents to be specified by a user that are then interpreted by the robotic system to perform whole body manipulation tasks autonomously. We use a concept of "behaviors" to chain together sequences of "actions" for the robot to perform which is then executed real time.

Hebert, Paul↗
NASA NTRS1990

Algorithms and architectures for robot vision

The scope of the current work is to develop practical sensing implementations for robots operating in complex, partially unstructured environments. A focus in this work is to develop object models and estimation techniques which are specific to requirements of robot locomotion, approach and avoidance, and grasp and manipulation. Such problems have to date received limited attention in either computer or human vision - in essence, asking not only how perception is in general modeled, but also what is the functional purpose of its underlying representations. As in the past, researchers are drawing on ideas from both the psychological and machine vision literature. Of particular interest is the development 3-D shape and motion estimates for complex objects when given only partial and uncertain information and when such information is incrementally accrued over time. Current studies consider the use of surface motion, contour, and texture information, with the longer range goal of developing a fused sensing strategy based on these sources and others.

Schenker, Paul S.↗
NASA NTRS1991

RoMPS concept review automatic control of space robot

The Robot operated Material Processing in Space (RoMPS) experiment is being performed to explore the marriage of two emerging space commercialization technologies: materials processing in microgravity and robotics. This concept review presents engineering drawings and limited technical descriptions of the RoMPS programs' electrical and software systems.

Source record↗
NASA NTRS2005

Survey of Command Execution Systems for NASA Spacecraft and Robots

NASA spacecraft and robots operate at long distances from Earth Command sequences generated manually, or by automated planners on Earth, must eventually be executed autonomously onboard the spacecraft or robot. Software systems that execute commands onboard are known variously as execution systems, virtual machines, or sequence engines. Every robotic system requires some sort of execution system, but the level of autonomy and type of control they are designed for varies greatly. This paper presents a survey of execution systems with a focus on systems relevant to NASA missions.

Verma, Vandi↗
NASA NTRS2002

Space Operations Analysis Using the Synergistic Engineering Environment

The Synergistic Engineering Environment has been under development at the NASA Langley Research Center to aid in the understanding of the operations of spacecraft. This is accomplished through the integration of multiple data sets, analysis tools, spacecraft geometric models, and a visualization environment to create an interactive virtual simulation of the spacecraft. Initially designed to support the needs of the International Space Station, the SEE has broadened the scope to include spacecraft ranging from low-earth orbit to deep space missions. Analysis capabilities within the SEE include rigid body dynamics, kinematics, orbital mechanics, and payload operations. This provides the user the ability to perform real-time interactive engineering analyses in areas including flight attitudes and maneuvers, visiting vehicle docking scenarios, robotic operations, plume impingement, field of view obscuration, and alternative assembly configurations. The SEE has been used to aid in the understanding of several operational procedures related to the International Space Station. This paper will address the capabilities of the first build of the SEE, present several use cases of the SEE, and discuss the next build of the SEE.

Angster, Scott↗
NASA NTRS2006

Intelligent Vision Systems Independent Research and Development (IR&D) 2006

This report summarizes results in conduct of research sponsored by the 2006 Independent Research and Development (IR&D) program at Marshall Space Flight Center (MSFC) at Redstone Arsenal, Alabama. The focus of this IR&D is neural network (NN) technology provided by Imagination Engines, Incorporated (IEI) of St. Louis, Missouri. The technology already has many commercial, military, and governmental applications, and a rapidly growing list of other potential spin-offs. The goal for this IR&D is implementation and demonstration of the technology for autonomous robotic operations, first in software and ultimately in one or more hardware realizations. Testing is targeted specifically to the MSFC Flat Floor, but may also include other robotic platforms at MSFC, as time and funds permit. For the purpose of this report, the NN technology will be referred to by IEI's designation for a subset configuration of its patented technology suite: Self-Training Autonomous Neural Network Object (STANNO).

Patrick, Clinton↗
NASA NTRS2019

Planetary Rover Simulation for Lunar Exploration Missions

When planning planetary rover missions it is useful to develop intuition and skills driving in, quite literally, alien environments before incurring the cost of reaching said locales. Simulators make it possible to operate in environments that have the physical characteristics of target locations without the expense and overhead of extensive physical tests. To that end, NASA Ames and Open Robotics collaborated on a Lunar rover driving simulator based on the open source Gazebo simulation platform and leveraging ROS (Robotic Operating System) components. The simulator was integrated with research and mission software for rover driving, system monitoring, and science instrument simulation to constitute an end-to-end Lunar mission simulation capability. Although we expect our simulator to be applicable to arbitrary Lunar regions, we designed to a reference mission of prospecting in polar regions. The harsh lighting and low illumination angles at the Lunar poles combine with the unique reflectance properties of Lunar regolith to present a challenging visual environment for both human and computer perception. Our simulator placed an emphasis on high fidelity visual simulation in order to produce synthetic imagery suitable for evaluating human rover drivers with navigation tasks, as well as providing test data for computer vision software development.In this paper, we describe the software used to construct the simulated Lunar environment and the components of the driving simulation. Our synthetic terrain generation software artificially increases the resolution of Lunar digital elevation maps by fractal synthesis and inserts craters and rocks based on Lunar size-frequency distribution models. We describe the necessary enhancements to import large scale, high resolution terrains into Gazebo, as well as our approach to modeling the visual environment of the Lunar surface. An overview of the mission software system is provided, along with how ROS was used to emulate flight software components that had not been developed yet. Finally, we discuss the effect of using the high-fidelity synthetic Lunar images for visual odometry. We also characterize the wheel slip model, and find some inconsistencies in the produced wheel slip behaviour.

Allan, Mark↗
NASA NTRS2017

Supervisory Control of a Humanoid Robot in Microgravity for Manipulation Tasks

Teleoperation is the dominant form of dexterous robotic tasks in the field. However, there are many use cases in which direct teleoperation is not feasible such as disaster areas with poor communication as posed in the DARPA Robotics Challenge, or robot operations on spacecraft a large distance from Earth with long communication delays. Presented is a solution that combines the Affordance Template Framework for object interaction with TaskForce for supervisory control in order to accomplish high level task objectives with basic autonomous behavior from the robot. TaskForce, is a new commanding infrastructure that allows for optimal development of task execution, clear feedback to the user to aid in off-nominal situations, and the capability to add autonomous verification and corrective actions. This framework has allowed the robot to take corrective actions before requesting assistance from the user. This framework is demonstrated with Robonaut 2 removing a Cargo Transfer Bag from a simulated logistics resupply vehicle for spaceflight using a single operator command. This was executed with 80% success with no human involvement, and 95% success with limited human interaction. This technology sets the stage to do any number of high level tasks using a similar framework, allowing the robot to accomplish tasks with minimal to no human interaction.

Farrell, Logan C.↗
NASA NTRS1988

Second Annual Workshop on Space Operations Automation and Robotics (SOAR 1988)

Papers presented at the Second Annual Workshop on Space Operation Automation and Robotics (SOAR '88), hosted by Wright State University at Dayton, Ohio, on July 20, 21, 22, and 23, 1988, are documented herein. During the 4 days, approximately 100 technical papers were presented by experts from NASA, the USAF, universities, and technical companies. Panel discussions on Human Factors, Artificial Intelligence, Robotics, and Space Systems were held but are not documented herein. Technical topics addressed included knowledge-based systems, human factors, and robotics.

Griffin, Sandy↗
NASA NTRS1990

Third Annual Workshop on Space Operations Automation and Robotics (SOAR 1989)

Papers presented at the Third Annual Workshop on Space Operations Automation and Robotics (SOAR '89), hosted by the NASA Lyndon 8. Johnson Space Center at Houston, Texas, on July 25-27, 1989, are documented herein. During the three days, approximately 100 technical papers wer3 presented by experts from NASA, the USAF, universities, and technical companies. Also held were panel discussions on Air Force/NASA AI Overview and Expert System Verification and Validation. Tutorial sessions included Neural Networks; Theory and Application of Back Propagation; Verification and Validation of Expert Systems/ Evaluation of Expert System Tools; and Technical Environment for Modular Architectures for Robotics in Space; and are not documented herein. Technical topics addressed included intelligent systems, robotics, human factors, and environment.

Knowledge representation↗
NASA NTRS1992

ROMPS critical design review data package

The design elements of the Robot-Operated Material Processing in Space (ROMPS) system are described in outline and graphical form. The following subsystems/topics are addressed: servo system, testbed and simulation results, System V Controller, robot module, furnace module, SCL experiment supervisor and script sample processing control, battery system, watchdog timers, mechanical/thermal considerations, and fault conditions and recovery.

Dobbs, M. E.↗
NASA NTRSDate not supplied

Towards Autonomous Lunar Resource Excavation via Reinforcement Learning

To continue on a sustainable and flexible path, NASA needs to address the challenge of collecting and moving large amounts of regolith at the destination. NASA’s Regolith Advanced Surface Systems Operations Robot (RASSOR) is principally designed to mine and deliver regolith for In-Situ Resource Utilization (ISRU) processing. RASSOR’s design enables it to efficiently collect and deposit regolith, return collected material for processing, and myriad related ISRU activities. To reliably perform these operations on the lunar surface, RASSOR software and sensory systems need to be robust and maximize the information extracted from a reduced sensor payload. Herein, we present preliminary findings from the Intelligent Capabilities Enhanced RASSOR project. We created reduced-order simulation environments to develop autonomous trenching controllers via reinforcement learning and prototype state estimation architectures. The goal of reinforcement learning is for an agent to learn a policy (task strategy) through interactions with an environment. When the agent performs an action, a change occurs in environment state and a numerical reward is received which informs the agent whether the action performed was good or not. Since reinforcement learning algorithms learn through trial-and-error, a simulation is a desirable first environment for development and learning. We developed two simulations, the first is a 2D excavation simulation developed to facilitate parameter selection, and a 3D simulation developed using a game physics engine, to simulate simplified soil interactions and increase the fidelity of the dynamic models of the robotic agents. The development of this 3D simulation has enabled the training of additional sensing capabilities and research both at the granular mechanics and operations levels. We experimented with various virtual sensor payloads to identify a combination that enabled efficient excavation operation and learning. Our reward function is based on how much material is excavated per step. A penalty is also received for leaving the dig site and to smooth the acceleration of the drum arms. We implemented pseudo time-of-flight sensors to report distance from each drum to ground and the height above ground which was found to be more efficient than existing solutions. Our findings suggest that reinforcement learning for autonomous operations has learned viable trenching strategies within 3000 training episodes in our simplified 2D environment and helped identify desirable sensing capabilities, arrangements, and considerations such as the positioning of time-of-flight sensors. Future work includes expanding our simulation to more complex environments and scenarios, and transfer learning from simulation to RASSOR 2.0 hardware for deployment in the Regolith Test Bin at NASA's Kennedy Space Center.

rassor↗
NASA NTRS1992

ROMPS critical design review. Volume 1: Hardware

Topics concerning the Robot-Operated Material Processing in Space (ROMPS) Program are presented in viewgraph form and include the following: a systems overview; servocontrol and servomechanisms; testbed and simulation results; system V controller; robot module; furnace module; SCL experiment supervisor; SCL script sample processing control; SCL experiment supervisor fault handling; block diagrams; hitchhiker interfaces; battery systems; watchdog timers; mechanical/thermal systems; and fault conditions and recovery.

Dobbs, M. E.↗
NASA NTRS1993

Terminal Sliding Modes In Nonlinear Control Systems

Control systems of proposed type called "terminal controllers" offers increased precision and stability of robotic operations in presence of unknown and/or changing parameters. Systems include special computer hardware and software implementing novel control laws involving terminal sliding modes of motion: closed-loop combination of robot and terminal controller converge, in finite time, to point of stable equilibrium in abstract space of velocity and/or position coordinates applicable to particular control problem.

Venkataraman, Subramanian T.↗
NASA NTRS2008

Orbital Express Mission Operations Planning and Resource Management using ASPEN

The Orbital Express satellite servicing demonstrator program is a DARPA program aimed at developing "a safe and cost-effective approach to autonomously service satellites in orbit". The system consists of: a) the Autonomous Space Transport Robotic Operations (ASTRO) vehicle, under development by Boeing Integrated Defense Systems, and b) a prototype modular next-generation serviceable satellite, NEXTSat, being developed by Ball Aerospace. Flexibility of ASPEN: a) Accommodate changes to procedures; b) Accommodate changes to daily losses and gains; c) Responsive re-planning; and d) Critical to success of mission planning Auto-Generation of activity models: a) Created plans quickly; b) Repetition/Re-use of models each day; and c) Guarantees the AML syntax. One SRP per day vs. Tactical team

automated planning scheduling↗
NASA NTRS2009

Head Pose Estimation Using Multilinear Subspace Analysis for Robot Human Awareness

Mobile robots, operating in unconstrained indoor and outdoor environments, would benefit in many ways from perception of the human awareness around them. Knowledge of people's head pose and gaze directions would enable the robot to deduce which people are aware of the its presence, and to predict future motions of the people for better path planning. To make such inferences, requires estimating head pose on facial images that are combination of multiple varying factors, such as identity, appearance, head pose, and illumination. By applying multilinear algebra, the algebra of higher-order tensors, we can separate these factors and estimate head pose regardless of subject's identity or image conditions. Furthermore, we can automatically handle uncertainty in the size of the face and its location. We demonstrate a pipeline of on-the-move detection of pedestrians with a robot stereo vision system, segmentation of the head, and head pose estimation in cluttered urban street scenes.

Ivanov, Tonislav↗
NASA NTRS1995

STS-64 Space Shuttle mission report

The STS-64 Space Shuttle Program Mission Report summarizes the Payload activities as well as the Orbiter, External Tank (ET), Solid Rocket Booster (SRB), Redesigned Solid Rocket Motor (RSRM), and the Space Shuttle main engine (SSME) systems performance during the sixty-fourth flight of the Space Shuttle Program and the nineteenth flight of the Orbiter vehicle Discovery (OV-103). In addition to the Orbiter, the flight vehicle consisted of an ET that was designated ET-66; three SSMEs that were designated as serial numbers 2031, 2109, and 2029 in positions 1, 2, and 3, respectively; and two SRB's that were designated Bl-068. The RSRM's that were installed in each SRB were designated as 360L041 A for the left SRB, and 360L041 B for the right SRB. The primary objective of this flight was to successfully perform the planned operations of the Lidar In-Space Technology Experiment (LITE), and to deploy the Shuttle Pointed Autonomous Research Tool for Astronomy (SPARTAN) -201 payload. The secondary objectives were to perform the planned activities of the Robot Operated Materials Processing System (ROMPS), the Shuttle Amateur Radio Experiment - 2 (SAREX-2), the Solid Surface Combustion Experiment (SSCE), the Biological Research in Canisters (BRIC) experiment, the Radiation Monitoring Equipment-3 (RME-3) payload, the Military Application of Ship Tracks (MAST) experiment, and the Air Force Maui Optical Site Calibration Test (AMOS) payload.

Fricke, Robert W., Jr.↗
NASA NTRSDate not supplied

Autonomous Robotic Manipulator Software

Autonomous robotic manipulation requires a deep and wide stack of supporting software. This paper presents Autonomous Robotic Manipulator Software (ARMS), a software suite designed at NASA Langley Research Center to support research and development of different algorithms for In-space Servicing, Assembly and Manufacturing (ISAM). ARMS solves common challenges along the autonomous manipulation software stack. Various challenges, such as integration with commercial hardware, simulation, and path planning, are solved through the use of Robot Operating System 2 and its community-developed packages. Other challenges, such as configuration management and task definition, and execution are solved in software built on those tools. The result is a modular approach to robotic system definition, agent actions, and assembly task definitions. ARMS has been used in two ISAM projects at NASA Langley Research Center, the Precision Assembled Space Structures project and the Built On-orbit Robotically assembled Gigatruss project.

Collin J Cresta↗