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At least 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
NASA NTRS2008

Integrated Docking Simulation and Testing with the Johnson Space Center Six-Degree of Freedom Dynamic Test System

The Exploration Systems Architecture defines missions that require rendezvous, proximity operations, and docking (RPOD) of two spacecraft both in Low Earth Orbit (LEO) and in Low Lunar Orbit (LLO). Uncrewed spacecraft must perform automated and/or autonomous rendezvous, proximity operations and docking operations (commonly known as Automated Rendezvous and Docking, AR&D). The crewed versions may also perform AR&D, possibly with a different level of automation and/or autonomy, and must also provide the crew with relative navigation information for manual piloting. The capabilities of the RPOD sensors are critical to the success of the Constellation Program; this is carried as one of the CEV Project top risks. The Exploration Technology Development Program (ETDP) AR&D Sensor Technology Project seeks to reduce this risk by increasing technology maturation of selected relative navigation sensor technologies through testing and simulation. One of the project activities is a series of "pathfinder" testing and simulation activities to integrate relative navigation sensors with the Johnson Space Center Six-Degree-of-Freedom Test System (SDTS). The SDTS will be the primary testing location for the Orion spacecraft s Low Impact Docking System (LIDS). Project team members have integrated the Orion simulation with the SDTS computer system so that real-time closed loop testing can be performed with relative navigation sensors and the docking system in the loop during docking and undocking scenarios. Two relative navigation sensors are being used as part of a "pathfinder" activity in order to pave the way for future testing with the actual Orion sensors. This paper describes the test configuration and test results.

Mitchell, Jennifer D.↗
NASA NTRS2008

Orbital Express Advanced Video Guidance Sensor: Ground Testing, Flight Results and Comparisons

Orbital Express (OE) was a successful mission demonstrating automated rendezvous and docking. The 2007 mission consisted of two spacecraft, the Autonomous Space Transport Robotic Operations (ASTRO) and the Next Generation Serviceable Satellite (NEXTSat) that were designed to work together and test a variety of service operations in orbit. The Advanced Video Guidance Sensor, AVGS, was included as one of the primary proximity navigation sensors on board the ASTRO. The AVGS was one of four sensors that provided relative position and attitude between the two vehicles. Marshall Space Flight Center was responsible for the AVGS software and testing (especially the extensive ground testing), flight operations support, and analyzing the flight data. This paper briefly describes the historical mission, the data taken on-orbit, the ground testing that occurred, and finally comparisons between flight data and ground test data for two different flight regimes.

Pinson, Robin M.↗
NASA NTRS2012

Method and system for providing autonomous control of a platform

The present application provides a system for enabling instrument placement from distances on the order of five meters, for example, and increases accuracy of the instrument placement relative to visually-specified targets. The system provides precision control of a mobile base of a rover and onboard manipulators (e.g., robotic arms) relative to a visually-specified target using one or more sets of cameras. The system automatically compensates for wheel slippage and kinematic inaccuracy ensuring accurate placement (on the order of 2 mm, for example) of the instrument relative to the target. The system provides the ability for autonomous instrument placement by controlling both the base of the rover and the onboard manipulator using a single set of cameras. To extend the distance from which the placement can be completed to nearly five meters, target information may be transferred from navigation cameras (used for long-range) to front hazard cameras (used for positioning the manipulator).

Seelinger, Michael J.↗
NASA NTRS2007

Automated Rendezvous and Docking Sensor Testing at the Flight Robotics Laboratory

The Exploration Systems Architecture defines missions that require rendezvous, proximity operations, and docking (RPOD) of two spacecraft both in Low Earth Orbit (LEO) and in Low Lunar Orbit (LLO). Uncrewed spacecraft must perform automated and/or autonomous rendezvous, proximity operations and docking operations (commonly known as Automated Rendezvous and Docking, (AR&D).) The crewed versions of the spacecraft may also perform AR&D, possibly with a different level of automation and/or autonomy, and must also provide the crew with relative navigation information for manual piloting. The capabilities of the RPOD sensors are critical to the success of the Exploration Program. NASA has the responsibility to determine whether the Crew Exploration Vehicle (CEV) contractor-proposed relative navigation sensor suite will meet the CEV requirements. The relatively low technology readiness of relative navigation sensors for AR&D has been carried as one of the CEV Projects top risks. The AR&D Sensor Technology Project seeks to reduce this risk by increasing technology maturation of selected relative navigation sensor technologies through testing and simulation, and to allow the CEV Project to assess the relative navigation sensors.

Howard, Richard T.↗
NASA NTRSDate not supplied

Moon to Mars Planetary Autonomous Construction Technology (MMPACT) Lunar Surface Construction Activity at NASA Marshall Space Flight Center

Introduction: The goal of the Moon to Mars Planetary Autonomous Construction Technology (MMPACT) Project at NASA Marshall Space Flight Center (MSFC) is to develop, deliver, and demonstrate on-demand capabilities to protect astronauts and create infrastructure elements on the lunar surface via construction of landing pads, habitats, shelters, roadways, berms, and blast shields using lunar regolith-based materials. MSFC has strong collaborations with industry, academia, and other NASA Centers to accomplish this goal. The MMPACT project consists of three elements. The first focuses on the development of an autonomous construction system. The second focuses on construction feedstock materials development. The third element focuses on the development of a microwave sintering construction capability. The team plans to demonstrate construction on a small Commercial Lunar Payload Services (CLPS) lander in the 2025 timeframe, with a future goal of constructing a subscale landing pad in 2028-2029.The MMPACT project is funded through the Lunar Surface Innovation Initiative, which is part of the Space Technology Mission Directorate. Technology Development: The MMPACT team will evaluate multiple autonomous construction and microwave construction technologies, materials, and construction element forms. Selected technologies will be matured; processes and operations will be defined for the two flight missions. Evaluations of materials, as well as the technology itself, will be demonstrated in simulated lunar environments as part of the technology maturation process. The team is keenly aware of the properties of the lunar environment. Its temperature swings, negligible exosphere, and unprepared site foundations factor into the materials for both construction and hardware, the concept of operations, and the technology’s interdependencies. Materials: The team is looking at materials that can be produced from in-situ resources in an effort to make lunar construction cost-effective. The particular focus of the materials team is cementitious materials, metals, and sintered and melted regolith. These materials will be studied for tensile, compressive, and flexural strength. They will also be tested for their ability to handle thermal swings and vacuum. They will be fully characterized using various microscopy techniques to examine micro-structures, chemistry, and crystal formation. Interdependencies: There are many interdependencies that MMPACT has already identified. These include: •Excavation interface •Regolith feedstock beneficiation •Regolith feedstock storage and provision •Requirements for structures •Site-to-site mobility systems •Availability of lunar simulant •Lander off-loading capabilities •Navigation systems •Power •Regolith composition and mineralogy •Lander specifications •Communication protocols Technology developments in these additional areas would be beneficial to MMPACT.

Moon to Mars Planetary Autonomous Construction Tec↗
NASA NTRS2009

Power Sources for Micro-Autonomous Vehicles- Challenges and Prospects

Micro-autonomous vehicle systems are expected to have expanded role in military missions by providing full spectrum intelligence, surveillance and reconnaissance support on the battlefield, suppression of enemy defenses, and enabling co-operative (swarm-like) configurations. Of the numerous demanding requirements of autonomy, sensing, navigation, mobility, etc., meeting the requirement of mission duration or endurance is a very challenging one. This requirement is demanding because of the constraints of mass and volume that limit the quantity of energy that can be stored on-board. Energy is required for mobility, payload operation, information processing, and communication. Mobility requirements typically place an extraordinary demand on the specific energy (Wh/kg) and specific power (W/kg) of the power source; the actual distribution of the energy between mobility and other system functions could vary substantially with the mission type. The power requirements for continuous mobility can vary from 100-1000 W/kg depending on the terrain, ground speed and flight speed. Even with the power source accounting for 30% of the mass of the vehicle, the best of rechargeable batteries can provide only up to 1-2 hours of run-time for a continuous power demand at 100W/kg. In the case of micro-aerial vehicles with flight speed requirements in the range of 5-15 m s-1, the mission times rarely exceed 20 minutes [2]. Further, the power required during take-off and hover can be twice or thrice that needed for steady level flight, and thus the number and sequence of such events is also limited by the mass and size of the power source. For operations such as "perch and stare" or "silent watch" the power demand is often only a tenth of that required during continuous flight. Thus, variation in power demand during various phases of the mission importantly affects the power source selection.

fuel cells↗
NASA NTRS2012

Morpheus: Advancing Technologies for Human Exploration

NASA's Morpheus Project has developed and tested a prototype planetary lander capable of vertical takeoff and landing. Designed to serve as a vertical testbed (VTB) for advanced spacecraft technologies, the vehicle provides a platform for bringing technologies from the laboratory into an integrated flight system at relatively low cost. This allows individual technologies to mature into capabilities that can be incorporated into human exploration missions. The Morpheus vehicle is propelled by a LOX/Methane engine and sized to carry a payload of 1100 lb to the lunar surface. In addition to VTB vehicles, the Project s major elements include ground support systems and an operations facility. Initial testing will demonstrate technologies used to perform autonomous hazard avoidance and precision landing on a lunar or other planetary surface. The Morpheus vehicle successfully performed a set of integrated vehicle test flights including hot-fire and tethered hover tests, leading up to un-tethered free-flights. The initial phase of this development and testing campaign is being conducted on-site at the Johnson Space Center (JSC), with the first fully integrated vehicle firing its engine less than one year after project initiation. Designed, developed, manufactured and operated in-house by engineers at JSC, the Morpheus Project represents an unprecedented departure from recent NASA programs that traditionally require longer, more expensive development lifecycles and testing at remote, dedicated testing facilities. Morpheus testing includes three major types of integrated tests. A hot-fire (HF) is a static vehicle test of the LOX/Methane propulsion system. Tether tests (TT) have the vehicle suspended above the ground using a crane, which allows testing of the propulsion and integrated Guidance, Navigation, and Control (GN&C) in hovering flight without the risk of a vehicle departure or crash. Morpheus free-flights (FF) test the complete Morpheus system without the additional safeguards provided during tether. A variety of free-flight trajectories are planned to incrementally build up to a fully functional Morpheus lander capable of flying planetary landing trajectories. In FY12, these tests will culminate with autonomous flights simulating a 1 km lunar approach trajectory, hazard avoidance maneuvers and precision landing in a prepared hazard field at the Kennedy Space Center (KSC). This paper describes Morpheus integrated testing campaign, infrastructure, and facilities, and the payloads being incorporated on the vehicle. The Project s fast pace, rapid prototyping, frequent testing, and lessons learned depart from traditional engineering development at JSC. The Morpheus team employs lean, agile development with a guiding belief that technologies offer promise, but capabilities offer solutions, achievable without astronomical costs and timelines.

Olansen, Jon B.↗
NASA NTRS1989

Automation of orbit determination functions for National Aeronautics and Space Administration (NASA)-supported satellite missions

The Flight Dynamics Facility (FDF) at Goddard Space Flight Center (GSFC) provides spacecraft trajectory determination for a wide variety of National Aeronautics and Space Administration (NASA)-supported satellite missions, using the Tracking Data Relay Satellite System (TDRSS) and Ground Spaceflight and Tracking Data Network (GSTDN). To take advantage of computerized decision making processes that can be used in spacecraft navigation, the Orbit Determination Automation System (ODAS) was designed, developed, and implemented as a prototype system to automate orbit determination (OD) and orbit quality assurance (QA) functions performed by orbit operations. Based on a machine-resident generic schedule and predetermined mission-dependent QA criteria, ODAS autonomously activates an interface with the existing trajectory determination system using a batch least-squares differential correction algorithm to perform the basic OD functions. The computational parameters determined during the OD are processed to make computerized decisions regarding QA, and a controlled recovery process isactivated when the criteria are not satisfied. The complete cycle is autonomous and continuous. ODAS was extensively tested for performance under conditions resembling actual operational conditions and found to be effective and reliable for extended autonomous OD. Details of the system structure and function are discussed, and test results are presented.

Mardirossian, H.↗
NASA NTRS1990

Automation of orbit determination functions for National Aeronautics and Space Administration (NASA)-supported satellite missions

The Flight Dynamics Facility (FDF) at Goddard Space Flight Center (GSFC) provides spacecraft trajectory determination for a wide variety of National Aeronautics and Space Administration (NASA)-supported satellite missions, using the Tracking Data Relay Satellite System (TDRSS) and Ground Spaceflight and Tracking Data Network (GSTDN). To take advantage of computerized decision making processes that can be used in spacecraft navigation, the Orbit Determination Automation System (ODAS) was designed, developed, and implemented as a prototype system to automate orbit determination (OD) and orbit quality assurance (QA) functions performed by orbit operations. Based on a machine-resident generic schedule and predetermined mission-dependent QA criteria, ODAS autonomously activates an interface with the existing trajectory determination system using a batch least-squares differential correction algorithm to perform the basic OD functions. The computational parameters determined during the OD are processed to make computerized decisions regarding QA, and a controlled recovery process is activated when the criteria are not satisfied. The complete cycle is autonomous and continuous. ODAS was extensively tested for performance under conditions resembling actual operational conditions and found to be effective and reliable for extended autonomous OD. Details of the system structure and function are discussed, and test results are presented.

Mardirossian, H.↗
NASA NTRSDate not supplied

A Strategic Approach for Dense, Integrated, Vehicle Navigation

Drone usage has been on the rise in recent years with applications that include parcel delivery, wildlife protection, precision farming, law enforcement, and industrial inspection, just to name a few. This paper addresses drone separation in time and distance for high volume corridors (en route) and lanes (on ground), merging as well as crossing intersections of multiple corridors/lanes. There exists a gamut of approaches to solving merging and intersection crossing problems. At one end of the extreme are the conservative yet low cost and verifiable solutions of today that deal with two drones at a time. At the other end are complex Machine Learning-based solutions with high computing requirements for fully autonomous drones of the future that are expected to handle all contentions. This paper presents a feasible and verifiable strategic approach to solving these problems that is based on distributed cooperation between the UAVs/UASs and the infrastructure. Unlike existing centralized and pre-planned approaches, the proposed solution is fully distributed and enables autonomous aircraft to decide to adjust their speed and distance with respect to the preceding aircraft, dynamically. Three phases of the strategic approach (Prepare, Adjust, Commit) are presented. Simulation results are presented that show the proposed approach is stable and resilient to induced perturbations and guarantees a set of fixed-wing UAVs/UASs to merge and cross intersections by adjusting their speed based on their distance to the aircraft in front of them.

Distributed↗
NASA NTRS2012

Lunar Navigation Determination System - LaNDS

A portable comprehensive navigational system has been developed that both robotic and human explorers can use to determine their location, attitude, and heading anywhere on the lunar surface independent of external infrastructure (needs no Lunar satellite network, line of sight to the Sun or Earth, etc.). The system combines robust processing power with an extensive topographical database to create a real-time atlas (GIS Geospatial Information System) that is able to autonomously control and monitor both single unmanned rovers and fleets of rovers, as well as science payload stations. The system includes provisions for teleoperation and tele-presence. The system accepts (but does not require) inputs from a wide range of sensors. A means was needed to establish a location when the search is taken deep in a crater (looking for water ice) and out of view of Earth or any other references. A star camera can be employed to determine the user's attitude in menial space and stellar map in body space. A local nadir reference (e.g., an accelerometer that orients the nadir vector in body space) can be used in conjunction with a digital ephemeris and gravity model of the Moon to isolate the latitude, longitude, and azimuth of the user on the surface. That information can be used in conjunction with a Lunar GIS and advanced navigation planning algorithms to aid astronauts (or other assets) to navigate on the Lunar surface.

Quinn, David↗
NASA NTRSDate not supplied

A Structurally-Adaptive Framework for Distributed Airborne Sensing over Real-time Collaborative Information Sharing Networks

The emergence and maturation of wireless communication technologies continue to transform the aviation industry and are enabling new solutions to challenges faced by NASA’s Advanced Air Mobility (AAM) initiative. AAM is leading towards high-density autonomous aircraft operations in areas underserved by traditional aviation, such as over densely populated urban centers. In this paper, we build on concepts from distributed sensing and smart spaces - where sensing, processing, and communication are embedded in an environment, and agents are operating within the space can exploit these capabilities in real-time through collaborative information sharing networks. Building from these concepts, we propose a framework to enable a dynamic, topologically-adaptive, and distributed estimation system for man-rated aviation to address challenges faced by autonomous AAM operations. This paper presents the initial concept of operations and system design for this framework, presents a mathematical formulation for abstraction of the problem, identifies requirements and constraints for operation, and presents algorithmic constructs to demonstrate operation. The initial framework design will focus on supporting precision navigation and independent surveillance supporting conformance monitoring of aircraft in airspace corridors and vertiport airspaces. Preliminary results from this framework shows promise in addressing gaps in current technologies needed to enable future AAM concepts, while promising greater capabilities, performance, robustness, and safety over current aviation systems and operations.

Distributed sensing↗
NASA NTRS1991

Generalized Covariance Analysis For Remote Estimators

Technique developed to predict true covariance of stochastic process at remote location when control applied to process both by autonomous (local-estimator) control subsystem and remote (non-local-estimator) control subsystem. Intended orginally for design and evaluation of ground-based schemes for estimation of gyro parameters of Magellan spacecraft. Applications include variety of remote-control systems with and without delays. Potential terrestrial applications include navigation and control of industrial processes.

Boone, Jack N.↗
NASA NTRS2006

Detection of Water Hazards for Autonomous Robotic Vehicles

Four methods of detection of bodies of water are under development as means to enable autonomous robotic ground vehicles to avoid water hazards when traversing off-road terrain. The methods involve processing of digitized outputs of optoelectronic sensors aboard the vehicles. It is planned to implement these methods in hardware and software that would operate in conjunction with the hardware and software for navigation and for avoidance of solid terrain obstacles and hazards. The first method, intended for use during the day, is based on the observation that, under most off-road conditions, reflections of sky from water are easily discriminated from the adjacent terrain by their color and brightness, regardless of the weather and of the state of surface waves on the water. Accordingly, this method involves collection of color imagery by a video camera and processing of the image data by an algorithm that classifies each pixel as soil, water, or vegetation according to its color and brightness values (see figure). Among the issues that arise is the fact that in the presence of reflections of objects on the opposite shore, it is difficult to distinguish water by color and brightness alone. Another issue is that once a body of water has been identified by means of color and brightness, its boundary must be mapped for use in navigation. Techniques for addressing these issues are under investigation. The second method, which is not limited by time of day, is based on the observation that ladar returns from bodies of water are usually too weak to be detected. In this method, ladar scans of the terrain are analyzed for returns and the absence thereof. In appropriate regions, the presence of water can be inferred from the absence of returns. Under some conditions in which reflections from the bottom are detectable, ladar returns could, in principle, be used to determine depth. The third method involves the recognition of bodies of water as dark areas in short-wavelength infrared (SWIR) images. This method is based on the fact, well known among experts in remote sensing, that water bodies of any appreciable depth appear very dark in near-infrared, overhead imagery. Even under a thick layer of marine fog, SWIR illumination is present. Hence, this method may work even in the presence of clouds, though it is unlikely to work at night. Snow and ice also exhibit very strong absorption at wavelengths greater than about 1.4 m. Hence, the wavelength range of about 1.5 to 1.6 m might be useable in this method for recognizing water, snow, and ice. One notable drawback of this method is that useful look-ahead distance could be limited by surface reflections. The fourth method, intended for use at night, involves the contrast between water and terrain in thermal-infrared (medium-wavelength infrared) imagery. This method is based on the fact that at night, water is usually warmer than the adjacent terrain. Look-ahead distance could be limited in this method because, for reasons not yet fully understood, water appears to darken in the thermal infrared with increasing distance.

Matthes, Larry↗
NASA NTRSDate not supplied

Mapping the ISS with the Autonomous Free-Flying Astrobee Robots

Space intra-vehicular robots (IVR) can provide coverage survey and targeted close-up inspection imagery capabilities that improve ground operator situation awareness. We report on a series of activities using the autonomous Astrobee robotic free-flyers that mapped the interior of three ISS modules, generating a panoramic tour of the ISS interior without astronaut manual photography. For the ISS, these capabilities can complement or replace time-consuming crew inspection tasks, providing panoramic data products that are easier to navigate than the baseline crew safety videos. For future exploration vehicles with extended uncrewed mission phases, robotic inspection will play a more critical role of providing the primary “eyes in the sky” for vehicle operators.

robotics↗
NASA NTRS2017

Development of Increasingly Autonomous Traffic Data Manager Using Pilot Relevancy and Ranking Data

NASA's Safe Autonomous Systems Operations (SASO) project goal is to define and safely enable all future airspace operations by justifiable and optimal autonomy for advanced air, ground, and connected capabilities. This work showcases how Increasingly Autonomous Systems (IAS) could create operational transformations beneficial to the enhancement of civil aviation safety and efficiency. One such IAS under development is the Traffic Data Manager (TDM). This concept is a prototype 'intelligent party-line' system that would declutter and parse out non-relevant air traffic, displaying only relevant air traffic to the aircrew in a digital data communications (Data Comm) environment. As an initial step, over 22,000 data points were gathered from 31 Airline Transport Pilots to train the machine learning algorithms designed to mimic human experts and expertise. The test collection used an analog of the Navigation Display. Pilots were asked to rate the relevancy of the displayed traffic using an interactive tablet application. Pilots were also asked to rank the order of importance of the information given, to better weight the variables within the algorithm. They were also asked if the information given was enough data, and more importantly the "right" data to best inform the algorithm. The paper will describe the findings and their impact to the further development of the algorithm for TDM and, in general, address the issue of how can we train supervised machine learning algorithms, critical to increasingly autonomous systems, with the knowledge and expertise of expert human pilots.

Le Vie, Lisa R.↗
NASA NTRS1988

Applications of concurrent neuromorphic algorithms for autonomous robots

This article provides an overview of studies at the Oak Ridge National Laboratory (ORNL) of neural networks running on parallel machines applied to the problems of autonomous robotics. The first section provides the motivation for our work in autonomous robotics and introduces the computational hardware in use. Section 2 presents two theorems concerning the storage capacity and stability of neural networks. Section 3 presents a novel load-balancing algorithm implemented with a neural network. Section 4 introduces the robotics test bed now in place. Section 5 concerns navigation issues in the test-bed system. Finally, Section 6 presents a frequency-coded network model and shows how Darwinian techniques are applied to issues of parameter optimization and on-line design.

Barhen, J.↗