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At least 253 records · Page 14

Interplanetary optical navigation - Voyager Uranus encounter

Optical observations were necessary during the Voyager II Uranus approach and encounter in order to meet mission navigational requirements. In this paper, the overall optical navigation system and operation at Uranus are outlined. The formulation of the optical observables are presented, as well as their subsequent integration into the overall orbit determination products. Optical data analysis and some resulting orbit determination results from Uranus are shown. Certain scientifically important results were natural outcomes of the optical data analysis, and these are also reviewed. Finally, certain optical navigation related problems are anticipated at the forthcoming Neptune encounter in 1989; these are briefly discussed as are plans to deal with them.

Synnott, S. P.↗

Space Shuttle navigation validation

The validation of the guidance, navigation, and control system of the Space Shuttle is explained. The functions of the ascent, on-board, and entry mission phases software of the navigation system are described. The common facility testing, which evaluates the simulations to be used in the navigation validation, is examined. The standard preflight analysis of the operational modes of the navigation software and the post-flight navigation analysis are explained. The conversion of the data into a useful reference frame and the use of orbit parameters in the analysis of the data are discussed. Upon entry the data received are converted to flags, ratios, and residuals in order to evaluate performance and detect errors. Various programs developed to support navigation validation are explained. A number of events that occurred with the Space Shuttle's navigation system are described.

Ragsdale, A.↗

Autonomous Small Body Mapping and Spacecraft Navigation

Current methods for pose and shape estimation of small bodies, such as comets and asteroids, rely on extensive ground support and significant use of radiometric measurements using the Deep Space Network. The Stereo-Photoclinometry (SPC) technique is currently used to provide detailed topological information about a small body as well as its absolute orientation and position. While this technique has produced very accurate estimates, the core algorithm cannot be run in real-time and requires a team of scientists on the ground who must communicate with the spacecraft in order to oversee SPC operations. Autonomous onboard navigation addresses these limitations by eliminating the need for human oversight. In this paper, we present an optimization-based estimation algorithm for navigation that allows the spacecraft to autonomously approach and maneuver around an unknown small body by mapping its geometric shape, estimating its orientation, and simultaneously determining the trajectory of the center of mass of the small body. We show the effectiveness of the proposed algorithm using simulated data from a previous flight mission to Comet 67P.

Bhaskaran, Shyamkumar↗

Autonomous Navigation Results from the Mars Exploration Rover (MER) Mission

In January, 2004, the Mars Exploration Rover (MER) mission landed two rovers, Spirit and Opportunity, on the surface of Mars. Several autonomous navigation capabilities were employed in space for the first time in this mission. ]n the Entry, Descent, and Landing (EDL) phase, both landers used a vision system called the, Descent Image Motion Estimation System (DIMES) to estimate horizontal velocity during the last 2000 meters (m) of descent, by tracking features on the ground with a downlooking camera, in order to control retro-rocket firing to reduce horizontal velocity before impact. During surface operations, the rovers navigate autonomously using stereo vision for local terrain mapping and a local, reactive planning algorithm called Grid-based Estimation of Surface Traversability Applied to Local Terrain (GESTALT) for obstacle avoidance. ]n areas of high slip, stereo vision-based visual odometry has been used to estimate rover motion, As of mid-June, Spirit had traversed 3405 m, of which 1253 m were done autonomously; Opportunity had traversed 1264 m, of which 224 m were autonomous. These results have contributed substantially to the success of the mission and paved the way for increased levels of autonomy in future missions.

autonomous↗

Miniature EVA Software Defined Radio

As NASA embarks upon developing the Next-Generation Extra Vehicular Activity (EVA) Radio for deep space exploration, the demands on EVA battery life will substantially increase. The number of modes and frequency bands required will continue to grow in order to enable efficient and complex multi-mode operations including communications, navigation, and tracking applications. Whether conducting astronaut excursions, communicating to soldiers, or first responders responding to emergency hazards, NASA has developed an innovative, affordable, miniaturized, power-efficient software defined radio that offers unprecedented power-efficient flexibility. This lightweight, programmable, S-band, multi-service, frequency- agile EVA software defined radio (SDR) supports data, telemetry, voice, and both standard and high-definition video. Features include a modular design, an easily scalable architecture, and the EVA SDR allows for both stationary and mobile battery powered handheld operations. Currently, the radio is equipped with an S-band RF section. However, its scalable architecture can accommodate multiple RF sections simultaneously to cover multiple frequency bands. The EVA SDR also supports multiple network protocols. It currently implements a Hybrid Mesh Network based on the 802.11s open standard protocol. The radio targets RF channel data rates up to 20 Mbps and can be equipped with a real-time operating system (RTOS) that can be switched off for power-aware applications. The EVA SDR's modular design permits implementation of the same hardware at all Network Nodes concept. This approach assures the portability of the same software into any radio in the system. It also brings several benefits to the entire system including reducing system maintenance, system complexity, and development cost.

Pozhidaev, Aleksey↗

Phenomena-based graph representations and applications to chemical process simulation

Rapid and robust simulation of chemical processes is critical to conduct process design, optimization, techno-economic analysis, and sustainability analysis. Yet, efficiently solving simulation models remains a challenge due to the highly coupled and nonlinear nature of the underlying algebraic equations that capture the physical phenomena taking place in the process (e.g., material and energy conservation, phase equilibrium, reactions). In this work, we show that graph-theoretic representations of the physical phenomena within unit operations can help navigate and decompose equations to systematically identify alternative approaches for fast and robust numerical solutions. Specifically, we present a graph-theoretic abstraction that captures the connectivity between the model variables/equations and use this abstraction to group variables/equations into fundamental phenomena. We show that phenomena-based decomposition of the underlying equations can help decouple nonlinearities and enforce material/energy conservation at the process level to accelerate convergence. The proposed decomposition approach differs from the more traditional sequential modular simulation approach, in which equations are grouped and decomposed by unit operations. We implemented the phenomena-based decomposition in BioSTEAM—an open-source process simulation platform in Python—and demonstrated that this approach can converge a variety of separation process models. Compared to sequential modular simulation, the phenomena-based approach can converge idealized systems faster, but it can be slower for (or even fail to converge) highly coupled and nonideal process systems.

Convergence↗

Route Generation for a Synthetic Character (BOT) Using a Partial or Incomplete Knowledge Route Generation Algorithm in UT2004 Virtual Environment

This paper presents a new Route Generation Algorithm that accurately and realistically represents human route planning and navigation for Military Operations in Urban Terrain (MOUT). The accuracy of this algorithm in representing human behavior is measured using the Unreal Tournament(Trademark) 2004 (UT2004) Game Engine to provide the simulation environment in which the differences between the routes taken by the human player and those of a Synthetic Agent (BOT) executing the A-star algorithm and the new Route Generation Algorithm can be compared. The new Route Generation Algorithm computes the BOT route based on partial or incomplete knowledge received from the UT2004 game engine during game play. To allow BOT navigation to occur continuously throughout the game play with incomplete knowledge of the terrain, a spatial network model of the UT2004 MOUT terrain is captured and stored in an Oracle 11 9 Spatial Data Object (SOO). The SOO allows a partial data query to be executed to generate continuous route updates based on the terrain knowledge, and stored dynamic BOT, Player and environmental parameters returned by the query. The partial data query permits the dynamic adjustment of the planned routes by the Route Generation Algorithm based on the current state of the environment during a simulation. The dynamic nature of this algorithm more accurately allows the BOT to mimic the routes taken by the human executing under the same conditions thereby improving the realism of the BOT in a MOUT simulation environment.

Hanold, Gregg T.↗

Autonomy for Deep Space Communications and Navigation

In more than 50 years of deep space exploration, the number and complexity of the world’s space missions have continued to increase. This has placed increasing demands on deep space communication and navigation. However, these demands have been met in spite of an essentially level overall budget. One of the reasons for this this has been the continual application of autonomy in various forms. The NASA Deep Space Network’s implementation of “Fol-low the Sun Operations” is a recent example – but certainly not the only one. During this same period, the end-to-end information system has been increasingly automated through the application of packet telemetry and virtual channels. Fully autonomous spacecraft navigation has been demonstrated, and more limited autonomy has been applied to the operational navigation process. This paper will show how autonomy and automation capabilities have been infused into operational deep space mission communication and navigation in both flight and ground systems as appropriate. It will further discuss ongoing work aimed at increasing the level of autonomy in the future, leverag-ing recent research and application of autonomy in everyday life.

Chang, Susan↗

A Comprehensive Orbit Reconstruction for the Galileo Prime Mission in the J2000 System

The Galileo spacecraft arrived at Jupiter in December of 1995 to begin an orbital tour of the Jovian system. The objective of the tour was up close study of the planet, its satellites, and its magnetosphere. The spacecraft completed its 11 orbit prime mission in November of 1997 having had 16 successful close encounters with the Galilean satellites (including two prior to Jupiter orbit insertion). Galileo continues to operate and will have made an additional 10 orbits of Jupiter by the date of this Conference. Earlier papers discuss the determination of the spacecraft orbit in support of mission operations from arrival at Jupiter through the first 9 orbits. In this paper we re-examine those earlier orbits and extend the analysis through orbit 12, the first orbit of the Galileo Europa Mission (GEM). The objective of our work is the reconstruction of the spacecraft trajectory together with the development of a consistent set of ephemerides for the Galilean satellites. As a necessary byproduct of the reconstruction we determine improved values for the Jovian system gravitational parameters and for the Jupiter pole orientation angles. Our preliminary analyses have already led to many of the results reported in the scientific literature. Unlike the Galileo Navigation Team which operates in the EME-1950 coordinate system, we elected to work in the (J2000) International Celestial Reference Frame (ICRF), the reference frame of the current JPL planetary and satellite ephemerides as well as the standard frame of the international astronomical and planetary science community. Use of this frame permits more precise modelling of the spacecraft and satellite observations. Moreover, it is the frame of choice for all other operational JPL missions and will probably be the frame for future missions for some time. Consequently, our adoption of the ICRF will facilitate the combination of our results with any obtained from future missions (e.g. the proposed Europa Orbiter mission). In addition, our results may be used by the science community, without need of a reference frame conversion.

Jacobson, Robert A.↗

Regionalized Lunar South Pole Surface Navigation System Analysis

Apollo missions utilized Earth-based assets for navigation because the landings took place at lunar locations in constant view from the Earth. The new exploration campaign to the lunar south pole region will have limited Earth visibility, but the extent to which a navigation system comprised solely of Earth-based tracking stations will provide adequate navigation solutions in this region is unknown. This report presents a dilution-of-precision (DoP)-based, stationary surface navigation analysis of the performance of multiple lunar satellite constellations, Earth-based deep space network assets, and combinations thereof. Results show that kinematic and integrated solutions cannot be provided by the Earth-based deep space network stations. Also, the stationary surface navigation system needs to be operated either as a two-way navigation system or as a one-way navigation system with local terrain information, while the position solution is integrated over a short duration of time with navigation signals being provided by a lunar satellite constellation.

Welch, Bryan W.↗

Mars Exploration Rovers Landing Dispersion Analysis

Landing dispersion estimates for the Mars Exploration Rover missions were key elements in the site targeting process and in the evaluation of landing risk. This paper addresses the process and results of the landing dispersion analyses performed for both Spirit and Opportunity. The several contributors to landing dispersions (navigation and atmospheric uncertainties, spacecraft modeling, winds, and margins) are discussed, as are the analysis tools used. JPL's MarsLS program, a MATLAB-based landing dispersion visualization and statistical analysis tool, was used to calculate the probability of landing within hazardous areas. By convolving this with the probability of landing within flight system limits (in-spec landing) for each hazard area, a single overall measure of landing risk was calculated for each landing ellipse. In-spec probability contours were also generated, allowing a more synoptic view of site risks, illustrating the sensitivity to changes in landing location, and quantifying the possible consequences of anomalies such as incomplete maneuvers. Data and products required to support these analyses are described, including the landing footprints calculated by NASA Langley's POST program and JPL's AEPL program, cartographically registered base maps and hazard maps, and flight system estimates of in-spec landing probabilities for each hazard terrain type. Various factors encountered during operations, including evolving navigation estimates and changing atmospheric models, are discussed and final landing points are compared with approach estimates.

Knocke, Philip C.↗

Mars Exploration Rovers landing dispersion analysis

Landing dispersion estimates for the Mars Exploration Rover missions were key elements in the site targeting process and in the evaluation of landing risk. This paper addresses the process and results of the landing dispersion analyses performed for both Spirit and Opportunity. The several contributors to landing dispersions (navigation and atmospheric uncertainties, spacecraft modeling, winds, and margins) are discussed, as are the analysis tools used. The JPL MarsLS program, a MATLAB-based landing dispersion visualization and statistical analysis tool, was used to calculate the probability of landing within hazardous areas. By convolving this with the probability of landing within flight system limits (in-spec landing) for each hazard area, a single overall measure of landing risk was calculated for each landing ellipse. In-spec probability contours were also generated, allowing a more synoptic view of site risks, illustrating the sensitivity to changes in landing location, and quantifying the possible consequences of anomalies such as incomplete maneuvers. Data and products required to support these analyses are described, including the landing footprints calculated by NASA Langley's POST program and the JPL AEPL program, cartographically registered base maps and hazard maps, and flight system estimates of in-spec landing probabilities for each hazard terrain type. Various factors encountered during operations, including evolving navigation estimates and changing atmospheric models, are discussed and final landing points are compared with approach estimates.

Desai, Prasun N.↗

Mass Inferencing Model Creation and Deployment to the RASSOR Lunar Excavation Robot

The Regolith Advanced Surface Systems Operations Robot (RASSOR) Excavator is a mobile robotic bucket-drum excavator platform with a unique space regolith excavation capability. The Intelligent Capabilities Enhanced RASSOR research project developed functionality for estimating the quantity of regolith mass ingested during RASSOR operation, enhancing RASSOR’s ability to successfully complete In-Situ Resource Utilization (ISRU) missions. To teleoperate or run autonomously, it is crucial for the amount of regolith mass ingested to be available as a system state for efficient operation. For example, during autonomous operation, RASSOR should navigate and move to a processing plant to offload the collected regolith when the drums are full; without knowledge of the total mass in the drums, this type of high-level planning is not possible. Three distinct modeling approaches were employed in developing a mass inferencing approach that could work on RASSOR, none of which require modification to the hardware. All take in system states, such as arm/drum motor positions, velocities, currents, voltages, and robot pose, and output a mass prediction for each set of the robot’s bucket drums. The developed models run in real-time, outputting predictions for the front drum mass, rear drum mass, timestamp of the last prediction, and total drum mass (sum of front and rear) in RASSOR’s drums. Models deployed to the hardware have low error (<7.5% mean error over the mass range, and <2.6% mean error when drums are more than half full) when making predictions in real-time. Our modeling approach can be adapted to use lunar excavation data to create models that are reflective of RASSOR’s dynamics when operating on the lunar surface. The results of this work are promising and show that models can be developed to accurately estimate excavated regolith mass.

Bucket Drum Excavators↗

Mass Inferencing Model Creation and Deployment to the RASSOR Lunar Excavation Robot

The Regolith Advanced Surface Systems Operations Robot (RASSOR) Excavator is a mobile robotic bucket-drum excavator platform with a unique space regolith excavation capability. The Intelligent Capabilities Enhanced RASSOR research project developed functionality for estimating the quantity of regolith mass ingested during RASSOR operation, enhancing RASSOR’s ability to successfully complete In-Situ Resource Utilization (ISRU) missions. To teleoperate or run autonomously, it is crucial for the amount of regolith mass ingested to be available as a system state for efficient operation. For example, during autonomous operation, RASSOR should navigate and move to a processing plant to offload the collected regolith when the drums are full; without knowledge of the total mass in the drums, this type of high-level planning is not possible. Three distinct modeling approaches were employed in developing a mass inferencing approach that could work on RASSOR, none of which require modification to the hardware. All take in system states, such as arm/drum motor positions, velocities, currents, voltages, and robot pose, and output a mass prediction for each set of the robot’s bucket drums. The developed models run in real-time, outputting predictions for the front drum mass, rear drum mass, timestamp of the last prediction, and total drum mass (sum of front and rear) in RASSOR’s drums. Models deployed to the hardware have low error (<7.5% mean error over the mass range, and <2.6% mean error when drums are more than half full) when making predictions in real-time. Our modeling approach can be adapted to use lunar excavation data to create models that are reflective of RASSOR’s dynamics when operating on the lunar surface. The results of this work are promising and show that models can be developed to accurately estimate excavated regolith mass.

bucket drum excavators↗

Extreme Ultraviolet Variability Experiment (EVE) on the Solar Dynamics Observatory (SDO): Overview of Science Objectives, Instrument Design, Data Products, and Model Developments

The highly variable solar extreme ultraviolet (EUV) radiation is the major energy input to the Earth's upper atmosphere, strongly impacting the geospace environment, affecting satellite operations, communications, and navigation. The Extreme ultraviolet Variability Experiment (EVE) onboard the NASA Solar Dynamics Observatory (SDO) will measure the solar EUV irradiance from 0.1 to 105 nm with unprecedented spectral resolution (0.1 nm), temporal cadence (ten seconds), and accuracy (20%). EVE includes several irradiance instruments: The Multiple EUV Grating Spectrographs (MEGS)-A is a grazingincidence spectrograph that measures the solar EUV irradiance in the 5 to 37 nm range with 0.1-nm resolution, and the MEGS-B is a normal-incidence, dual-pass spectrograph that measures the solar EUV irradiance in the 35 to 105 nm range with 0.1-nm resolution. To provide MEGS in-flight calibration, the EUV SpectroPhotometer (ESP) measures the solar EUV irradiance in broadbands between 0.1 and 39 nm, and a MEGS-Photometer measures the Sun s bright hydrogen emission at 121.6 nm. The EVE data products include a near real-time space-weather product (Level 0C), which provides the solar EUV irradiance in specific bands and also spectra in 0.1-nm intervals with a cadence of one minute and with a time delay of less than 15 minutes. The EVE higher-level products are Level 2 with the solar EUV irradiance at higher time cadence (0.25 seconds for photometers and ten seconds for spectrographs) and Level 3 with averages of the solar irradiance over a day and over each one-hour period. The EVE team also plans to advance existing models of solar EUV irradiance and to operationally use the EVE measurements in models of Earth s ionosphere and thermosphere. Improved understanding of the evolution of solar flares and extending the various models to incorporate solar flare events are high priorities for the EVE team.

Woods, T. N.↗

Mass Inferencing Model Creation and Deployment to the RASSOR Lunar Excavation Robot

The Regolith Advanced Surface Systems Operations Robot (RASSOR) Excavator is a teleoperated mobile robotic platform with a unique space regolith excavation capability. The Intelligent Capabilities Enhanced RASSOR research project developed functionality for inferencing regolith mass ingested during RASSOR operation, enhancing RASSOR’s ability to successfully complete ISRU missions. To teleoperate or run autonomously, it is crucial for the quantity of regolith mass ingested by RASSOR to be available as a system state for efficient operation. For example, during autonomous operation, RASSOR should navigate and move to a processing plant to offload the collected regolith when the drums are full; without knowledge of how much mass is in the drums, this type of high-level planning is not possible. Four distinct modeling approaches were employed in developing a mass inferencing approach that could work on RASSOR. All take in system states, such as arm/drum positions, velocities, currents, voltages, and robot pose, and output a mass prediction for each set of the robot’s bucket drums.1) A neural network model that takes a vector of normalized system states; 2) A model that uses the integrated power consumption of an arm-raise (normalized by velocity); 3) A model that uses average drum current over a variable length interval of the drum disengaged from the surface; and 4) A real-time estimation model that aggregates excavation drum current. The developed models run in real time, outputting predictions for the front and rear drums, timestamp of the last prediction, and total mass in RASSOR’s drums. Further testing is required to validate the arm-raise model (2), though initial tests indicate reasonable performance (<10% mean error) on the hardware. The linear fit of average drum-current model (3) had a front value of r^2=0.99 and a rear value of r^2=0.98 on the validation dataset. This model currently has the best performance on unseen data. The real time model (4) is still in development, though initial results on a small subset of the training data show that it has high accuracy in predicting the increase in mass during excavation. Though work remains to be done with deploying a high-fidelity model to the physical system that makes predictions with error below the desired threshold, the modular architecture for model development allows quick adjustment of parameters to increase model fidelity. This architecture can also be adapted to use lunar excavation data to create models that are reflective of RASSOR’s dynamics when operating on the lunar surface. The results are promising as it has been shown that models can be developed that accurately estimate excavated regolith mass.

rassor↗

Comparison of Orion Vision Navigation Sensor Performance from STS-134 and the Space Operations Simulation Center

The Orion Multi-Purpose Crew Vehicle is a new spacecraft being designed by NASA and Lockheed Martin for future crewed exploration missions. The Vision Navigation Sensor is a Flash LIDAR that will be the primary relative navigation sensor for this vehicle. To obtain a better understanding of this sensor's performance, the Orion relative navigation team has performed both flight tests and ground tests. This paper summarizes and compares the performance results from the STS-134 flight test, called the Sensor Test for Orion RelNav Risk Mitigation (STORRM) Development Test Objective, and the ground tests at the Space Operations Simulation Center.

Christian, John A.↗

Implementing a Terrestrial Timing Solution: Best Practices

This document is an overview and guide on the concept of precision time and how an alternative terrestrial timing solution to the Global Positioning System (GPS) can be implemented. What follows is a set of recommendations and best practices, derived from research at Oak Ridge National Laboratory (ORNL), across industry, and within the U.S. government, to implement a system of precision timing synchronization to support resilient operations for the U.S. grid. Precision time is a fundamental necessity for operating the grid today, and it becomes even more important as the grid modernizes with clean energy sources, microgrids, and precision sensors placed throughout the grid system to ensure failureresistant operations. The Global Navigation Satellite System (GNSS), which includes (GPS), is currently the primary provider of precision timing today. A terrestrial-based system for time delivery and synchronization to augment GPS is outlined below and it would provide secure time, synchronized with Coordinated Universal Time (UTC), in the event of outages or other interruptions associated with time delivery.

47 OTHER INSTRUMENTATION↗