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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
NASA NTRS2009

GPS Navigation for the Magnetospheric Multi-Scale Mission

In 2014. NASA is scheduled to launch the Magnetospheric Multiscale Mission (MMS), a four-satellite formation designed to monitor fluctuations in the Earth's magnetosphere. This mission has two planned phases with different orbits (1? x 12Re and 1.2 x 25Re) to allow for varying science regions of interest. To minimize ground resources and to mitigate the probability of collisions between formation members, an on-board orbit determination system consisting of a Global Positioning System (GPS) receiver and crosslink transceiver was desired. Candidate sensors would be required to acquire GPS signals both below and above the constellation while spinning at three revolutions-per-minute (RPM) and exchanging state and science information among the constellation. The Intersatellite Ranging and Alarm System (IRAS), developed by Goddard Space Flight Center (GSFC) was selected to meet this challenge. IRAS leverages the eight years of development GSFC has invested in the Navigator GPS receiver and its spacecraft communication expertise, culminating in a sensor capable of absolute and relative navigation as well as intersatellite communication. The Navigator is a state-of-the-art receiver designed to acquire and track weak GPS signals down to -147dBm. This innovation allows the receiver to track both the main lobe and the much weaker side lobe signals. The Navigator's four antenna inputs and 24 tracking channels, together with customized hardware and software, allow it to seamlessly maintain visibility while rotating. Additionally, an extended Kalman filter provides autonomous, near real-time, absolute state and time estimates. The Navigator made its maiden voyage on the Space Shuttle during the Hubble Servicing Mission, and is scheduled to fly on MMS as well as the Global Precipitation Measurement Mission (GPM). Additionally, Navigator's acquisition engine will be featured in the receiver being developed for the Orion vehicle. The crosslink transceiver is a 1/4 Watt transmitter utilizing a TDMA schedule to distribute a science quality message to all constellation members every ten seconds. Additionally the system generates one-way range measurements between formation members which is used as input to the Kalman filter. In preparation for the MMS Preliminary Design Review (PDR), the Navigator was required to pass a series of Technology Readiness Level (TRL) tests to earn the necessary TRL-6 classification. The TRL-6 level is achieved by demonstrating a prototype unit in a relevant end-to-end environment. The IRAS unit was able to meet all requirements during the testing phase, and has thus been TRL-6 qualified

Bamford, William↗
NASA NTRS2016

Flight Analysis of an Autonomously Navigated Experimental Lander

First steps have been taken to qualify a family of parafoil systems capable of increasing the survivability and reusability of high-altitude balloon payloads. The research is motivated by the common risk facing balloon payloads where expensive flight hardware can often land in inaccessible areas that make them difficult or impossible to recover. The Autonomously Navigated Experimental Lander (ANGEL) flight test introduced a commercial Guided Parachute Aerial Delivery System (GPADS) to a previously untested environment at 108,000 feet Mean Sea Level (MSL) to determine its high-altitude survivability and capabilities. Following release, ANGEL descended under a drogue until approximately 25,000 feet, at which point the drogue was jettisoned and the main parachute was deployed, commencing navigation. Multiple data acquisition platforms were used to characterize the return-to-point technology performance and help determine its suitability for returning future scientific payloads ranging from 180 to 10,000 pounds to safer and more convenient landing locations. This report describes the test vehicle design, and summarizes the captured sensor data. Various post-flight analyses are used to quantify the systems performance, gondola load data, and serve as a reference point for subsequent missions.

Parafoil↗
NASA NTRSDate not supplied

Energy-Optimized Path Planning for Uas in Varying Winds Via Reinforcement Learning

In this paper we propose a reinforcement learning (RL) algorithm for path planning of Unmanned Aviation Vehicles (UAVs) under varying wind conditions. Solutions to UAV path planning problems are becoming increasingly necessary as autonomous UAVs continue to enter commercial and government spaces. Path-planning is inherently challenging, as UAVs need to account for dynamically changing flying conditions such as weather, obstacle or no-fly zones, degraded vehicle health, and off-nominal battery power consumption. Machine learning methods such as reinforcement learning (RL) have the potential to revolutionize how vehicles navigate in such uncertain environments. In this study, we compute UAV trajectories from a pre-determined starting position to a target cell within a 7X7 grid environment by optimizing parameters for mission assurance and safety limits in addition to the energy consumption and operation time. The UAV navigates the grid by taking actions to move in any of the eight cardinal and inter-cardinal directions, under constant thrust profile. The resultant UAV state is sampled from a probability distribution which accounts for the UAV’s action, local wind velocity, and the presence of obstacles or boundaries. As the unmanned airspace gets more complex due to multiple vehicles and environmental uncertainties, trade-offs between energy consumption, operation time, risk tolerance, and mission assurance need to be made. Our Markov Decision Process (MDP) environment model can capture any combination of these in the optimization objective, making it novel compared to other work in the field.

trajectory planning↗
NASA NTRS1989

Semi-autonomous design concepts for a Mars rover

Studies of rover mobility and surface rendezvous for a Mars Rover/Sample Return (MRSR) mission have been performed. The objective of these efforts has been to identify and address the most challenging issues and develop the most promising design options for the rover mobility and navigation subsystems. A series of trade studies culminated in three candidate design concepts: a large, but otherwise conventional four-wheel drive vehicle; a sophisticated and agile six-legged walking vehicle; and a greatly simplified legged vehicle. This paper summarizes the key design features for these three concepts. For each design, the approaches to vehicle locomotion, sensing and navigation are discussed first. Timelines are developed for the adopted concepts of operation which constrain the range of the remaining parameters. Estimates of rover mass, volume, and power are provided, and performance is projected for vehicle range, terrain traversibility and navigation accuracy.

Spiessbach, Andrew J.↗
NASA NTRSDate not supplied

Markov Decision Process based Trajectory Planning for UAVs under Uncertain Wind Conditions

In this paper we propose a Markov Decision Process (MDP) algorithm for path-planning of Unmanned Aviation Vehicles (UAVs) under varying wind conditions. Solutions to path-planning for UAVs are becoming increasingly necessary as autonomous UAVs continue to enter commercial and government spaces. Path-planning is inherently challenging, as UAVs needs to account for dynamically changing flying conditions such as weather, obstacle or no-fly zones, degraded vehicle health and off-nominal battery power consumption. Machine learning methods such as Markov Decision Process (MDPs) have the potential to revolutionize how vehicles navigate in such uncertain environments. Previous papers have demonstrated the use of MDPs to optimize UAV path-planning for energy consumption under time-varying wind distribution. In this study, UAV trajectories from a pre-determined waypoint to target cell, will be computed on a 7X7 grid environment by optimizing parameters for mission assurance and safety limits in addition to the energy consumption, and operation time. The UAV navigates the grid by taking actions to move in either of the eight cardinal and intercardinal directions, under constant thrust profile. The next state of the UAV is calculated by considering its action, transition probability, obstacle cells and the wind speed magnitude and direction. Both constant and stochastic wind will be considered in this paper, the parameters being extracted from real wind measurements in proximity to an experimental UAV flight. One of the studies to be demonstrated in this paper is that as the unmanned airspace gets more complex with multiple vehicles and environmental uncertainties, trade-offs between energy consumption, operation time, risk tolerance, and mission assurance needs to be made. Further, MDPs are capable of fast computation of UAV trajectories under varying wind, hence making them suitable for in-flight path planners.

decision-making↗
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 NTRS1992

Achieving safe autonomous landings on Mars using vision-based approaches

Autonomous landing capabilities will be critical to the success of planetary exploration missions, and in particular to the exploration of Mars. Past studies have indicated that the probability of failure associated with open-loop landings is unacceptably high. Two approaches to achieving autonomous landings with higher probabilities of success are currently under analysis. If a landing site has been certified as hazard free, then navigational aids can be used to facilitate a precision landing. When only limited surface knowledge is available and landing areas cannot be certified as hazard free, then a hazard detection and avoidance approach can be used, in which the vehicle selects hazard free landing sites in real-time during its descent. Issues pertinent to both approaches, including sensors and algorithms, are presented. Preliminary results indicate that one promising approach to achieving high accuracy precision landing is to correlate optical images of the terrain acquired during the terminal descent phase with a reference image. For hazard detection scenarios, a sensor suite comprised of a passive intensity sensor and a laser ranging sensor appears promising as a means of achieving robust landings.

Pien, Homer↗
NASA NTRS2007

Slip Validation and Prediction for Mars Exploration Rovers

This paper presents a novel technique to validate and predict the rover slips on Martian surface for NASA's Mars Exploration Rovers. Different from the traditional approach, the proposed method uses the actual velocity profile of the wheels and the digital elevation map (DEM) from the stereo images of the terrain to formulate simplified equations of motion of the rovers. A weighted factor to the wheel-ground speed from the empirical data comprises the velocity equations of the simplified differential-algebraic system of the rover motion. Applying the discretization operator to these equations, the full kinematics state of the rover is then resolved by the configuration kinematics solution in the Robot Sequencing and Visualization Program (RSVP). This method produced accurate simulation of the rover movements compared with these of the earth testing vehicle. Using the telemetry from the onboard Visual Odometry, the simulated rover path also compares well with the actual track of the vehicle. Preliminary results indicated that the proposed computational method is very effective in planning the path of the rovers on the high-slope areas.

Mars Rovers↗
NASA NTRS2011

Process Algebra Approach for Action Recognition in the Maritime Domain

The maritime environment poses a number of challenges for autonomous operation of surface boats. Among these challenges are the highly dynamic nature of the environment, the onboard sensing and reasoning requirements for obeying the navigational rules of the road, and the need for robust day/night hazard detection and avoidance. Development of full mission level autonomy entails addressing these challenges, coupled with inference of the tactical and strategic intent of possibly adversarial vehicles in the surrounding environment. This paper introduces PACIFIC (Process Algebra Capture of Intent From Information Content), an onboard system based on formal process algebras that is capable of extracting actions/activities from sensory inputs and reasoning within a mission context to ensure proper responses. PACIFIC is part of the Behavior Engine in CARACaS (Cognitive Architecture for Robotic Agent Command and Sensing), a system that is currently running on a number of U.S. Navy unmanned surface and underwater vehicles. Results from a series of experimental studies that demonstrate the effectiveness of the system are also presented.

process algebras↗
NASA NTRS2020

GN&C Sequencing for Orion Rendezvous, Proximity Operations, and Docking

As part of the Artemis program to return humans to the lunar surface, the National Aeronautics and Space Administration is planning to use the Orion Multi- Purpose Crew Vehicle to transport crew to a small orbital platform called Gate- way in cislunar space. To facilitate this activity, Orion is required to perform Rendezvous, Proximity Operations, and Docking (RPOD) with both the Gate- way and the launch vehicle upper stage. The Orion spacecraft uses sequencing in the form of Phases, Segments, Activities, and Modes (PSAM) to configure Guidance, Navigation, & Control (GN&C) software during each portion of the mission. Significant updates to Orion PSAM definitions are required for RPOD. This paper describes the process of defining these new sequencing elements, implementing them in prototype flight software, and testing them in an integrated simulation environment. First, requirements are specified to determine the nominal and off-nominal sequencing behavior necessary to complete the mission. These requirements also specify which software functions should be fully autonomous and which functions require manual interactions from crew or ground operators. Next, the RPOD concept of operations is defined with detailed events listed in a mission timeline. Third, a state machine diagram is developed to show all PSAM states, including all possible transitions between them. After this, the PSAM states and transitions are entered into a sequencing software emulator and parameter values and modes are defined for GN&C software elements. Finally, the PSAM architecture is tested within an integrated simulation environment by connecting it with prototypes of relevant GN&C flight software elements and with detailed vehicle models. After the sequencing design has been finalized and tested, it is implemented in flight software.

Schulte, Peter Z.↗
NASA NTRS1981

Autonomous navigation system

An inertial navigation system utilizing a servo-controlled two degree of freedom pendulum to obtain specific force components in the locally level coordinate system is described. The pendulum includes a leveling gyroscope and an azimuth gyroscope supported on a two gimbal system. The specific force components in the locally level coordinate system are converted to components in the geographical coordinate system by means of a single Euler transformation. The standard navigation equations are solved to determine longitudinal and lateral velocities. Finally, vehicle position is determined by a further integration.

Merhav, S. J.↗
NASA NTRS2002

Relative Navigation of Formation Flying Satellites

The Guidance, Navigation, and Control Center (GNCC) at Goddard Space Flight Center (GSFC) has successfully developed high-accuracy autonomous satellite navigation systems using the National Aeronautics and Space Administration's (NASA's) space and ground communications systems and the Global Positioning System (GPS). In addition, an autonomous navigation system that uses celestial object sensor measurements is currently under development and has been successfully tested using real Sun and Earth horizon measurements.The GNCC has developed advanced spacecraft systems that provide autonomous navigation and control of formation flyers in near-Earth, high-Earth, and libration point orbits. To support this effort, the GNCC is assessing the relative navigation accuracy achievable for proposed formations using GPS, intersatellite crosslink, ground-to-satellite Doppler, and celestial object sensor measurements. This paper evaluates the performance of these relative navigation approaches for three proposed missions with two or more vehicles maintaining relatively tight formations. High-fidelity simulations were performed to quantify the absolute and relative navigation accuracy as a function of navigation algorithm and measurement type. Realistically-simulated measurements were processed using the extended Kalman filter implemented in the GPS Enhanced Inboard Navigation System (GEONS) flight software developed by GSFC GNCC. Solutions obtained by simultaneously estimating all satellites in the formation were compared with the results obtained using a simpler approach based on differencing independently estimated state vectors.

Long, Anne↗
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 NTRS2013

ULTRA: Underwater Localization for Transit and Reconnaissance Autonomy

This software addresses the issue of underwater localization of unmanned vehicles and the inherent drift in their onboard sensors. The software gives a 2 to 3 factor of improvement over the state-of-the-art underwater localization algorithms. The software determines the localization (position, heading) of an AUV (autonomous underwater vehicle) in environments where there is no GPS signal. It accomplishes this using only the commanded position, onboard gyros/accelerometers, and the bathymetry of the bottom provided by an onboard sonar system. The software does not rely on an onboard bathymetry dataset, but instead incrementally determines the position of the AUV while mapping the bottom. In order to enable long-distance underwater navigation by AUVs, a localization method called ULTRA uses registration of the bathymetry data products produced by the onboard forward-looking sonar system for hazard avoidance during a transit to derive the motion and pose of the AUV in order to correct the DR (dead reckoning) estimates. The registration algorithm uses iterative point matching (IPM) combined with surface interpolation of the Iterative Closest Point (ICP) algorithm. This method was used previously at JPL for onboard unmanned ground vehicle localization, and has been optimized for efficient computational and memory use.

Huntsberger, Terrance L.↗
NASA NTRS1995

Fuzzy logic techniques for rendezvous and docking of two geostationary satellites

Large assemblings in space require the ability to manage rendezvous and docking operations. In future these techniques will be required for the gradual build up of big telecommunication platforms in the geostationary orbit. The paper discusses the use of fuzzy logic to model and implement a control system for the docking/berthing of two satellites in geostationary orbit. The system mounted in a chaser vehicle determines the actual state of both satellites and generates torques to execute maneuvers to establish the structural latching. The paper describes the proximity operations to collocate the two satellites in the same orbital window, the fuzzy guidance and navigation of the chaser approaching the target and the final Fuzzy berthing. The fuzzy logic system represents a knowledge based controller that realizes the close loop operations autonomously replacing the conventional control algorithms. The goal is to produce smooth control actions in the proximity of the target and during the docking to avoid disturbance torques in the final assembly orbit. The knowledge of the fuzzy controller consists of a data base of rules and the definitions of the fuzzy sets. The knowledge of an experienced spacecraft controller is captured into a set of rules forming the Rules Data Base.

Ortega, Guillermo↗
NASA NTRS2008

Multi-Sensor Testing for Automated Rendezvous and Docking Sensor Testing at the Flight Robotics Lab

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 AR&D). The crewed missions may also perform rendezvous and docking operations and may require different levels of automation and/or autonomy, and must provide the crew with relative navigation information for manual piloting. The capabilities of the RPOD sensors are critical to the success ofthe Exploration Program. NASA has the responsibility to determine whether the Crew Exploration Vehicle (CEV) contractor-proposed relative navigation sensor suite will meet the requirements. The relatively low technology readiness level of AR&D relative navigation sensors has been carried as one of the CEV Project's top risks. The AR&D Sensor Technology Project seeks to reduce the risk by the testing and analysis of selected relative navigation sensor technologies through hardware-in-the-Ioop testing and simulation. These activities will provide the CEV Project information to assess the relative navigation sensors maturity as well as demonstrate test methods and capabilities. The first year of this project focused on a series of "pathfinder" testing tasks to develop the test plans, test facility requirements, trajectories, math model architecture, simulation platform, and processes that will be used to evaluate the Contractor-proposed sensors. Four candidate sensors were used in the first phase of the testing. The second phase of testing used four sensors simultaneously: two Marshall Space Flight Center (MSFC) Advanced Video Guidance Sensors (AVGS), a laser-based video sensor that uses retroreflectors attached to the target vehicle, and two commercial laser range finders. The multi-sensor testing was conducted at MSFC's Flight Robotics Laboratory (FRL) using the FRL's 6-DOF gantry system, called the Dynamic Overhead Target System (DOTS). The target vehicle for "docking" in the laboratory was a mockup that was representative of the proposed CEV docking system, with added retroreflectors for the AVGS.' The multi-sensor test configuration used 35 open-loop test trajectories covering three major objectives: (l) sensor characterization trajectories designed to test a wide range of performance parameters; (2) CEV-specific trajectories designed to test performance during CEV-like approach and departure profiles; and (3) sensor characterization tests designed for evaluating sensor performance under more extreme conditions as might be induced during a spacecraft failure or during contingency situations. This paper describes the test development, test facility, test preparations, test execution, and test results of the multisensor series oftrajectories

Brewster, Linda L.↗
NASA NTRS2011

Orion Handling Qualities During ISS Proximity Operations and Docking

NASA's Orion spacecraft is designed to autonomously rendezvous and dock with many vehicles including the International Space Station. However, the crew is able to assume manual control of the vehicle s attitude and flight path. In these instances, Orion must meet handling qualities requirements established by NASA. Two handling qualities assessments were conducted at the Johnson Space Center to evaluate preliminary designs of the vehicle using a six degree of freedom, high-fidelity guidance, navigation, and control simulation. The first assessed Orion s handling qualities during the last 20 ft before docking, and included both steady and oscillatory motions of the docking target. The second focused on manual acquisition of the docking axis during the proximity operations phase and subsequent station-keeping. Cooper-Harper handling qualities ratings, workload ratings and comments were provided by 10 evaluation pilots for the docking study and 5 evaluation pilots for the proximity operations study. For the docking task, both cases received 90% Level 1 (satisfactory) handling qualities ratings, exceeding NASA s requirement. All ratings for the ProxOps task were Level 1. These evaluations indicate that Orion is on course to meet NASA's handling quality requirements for ProxOps and docking.

Stephens, John-Paul↗
NASA NTRS2007

Multi-Sensor Testing for 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 AR&D). The crewed missions may also perform rendezvous and docking operations and may require different levels of automation and/or autonomy, and must 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 requirements. The relatively low technology readiness level of AR&D relative navigation sensors has been carried as one of the CEV Project's top risks. The AR&D Sensor Technology Project seeks to reduce the risk by the testing and analysis of selected relative navigation sensor technologies through hardware-in-the-loop testing and simulation. These activities will provide the CEV Project information to assess the relative navigation sensors maturity as well as demonstrate test methods and capabilities. The first year of this project focused on a series of"pathfinder" testing tasks to develop the test plans, test facility requirements, trajectories, math model architecture, simulation platform, and processes that will be used to evaluate the Contractor-proposed sensors. Four candidate sensors were used in the first phase of the testing. The second phase of testing used four sensors simultaneously: two Marshall Space Flight Center (MSFC) Advanced Video Guidance Sensors (AVGS), a laser-based video sensor that uses retroreflectors attached to the target vehicle, and two commercial laser range finders. The multi-sensor testing was conducted at MSFC's Flight Robotics Laboratory (FRL) using the FRL's 6-DOF gantry system, called the Dynamic Overhead Target System (DOTS). The target vehicle for "docking" in the laboratory was a mockup that was representative of the proposed CEV docking system, with added retroreflectors for the AVGS. The multi-sensor test configuration used 35 open-loop test trajectories covering three major objectives: (1) sensor characterization trajectories designed to test a wide range of performance parameters; (2) CEV-specific trajectories designed to test performance during CEV-like approach and departure profiles; and (3) sensor characterization tests designed for evaluating sensor performance under more extreme conditions as might be induced during a spacecraft failure or during contingency situations. This paper describes the test development, test facility, test preparations, test execution, and test results of the multi-sensor series of trajectories.

Brewster, L.↗