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At least 163 records · Page 9

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NASA NTRS2008

Improved Airborne System for Sensing Wildfires

The Wildfire Airborne Sensing Program (WASP) is engaged in a continuing effort to develop an improved airborne instrumentation system for sensing wildfires. The system could also be used for other aerial-imaging applications, including mapping and military surveillance. Unlike prior airborne fire-detection instrumentation systems, the WASP system would not be based on custom-made multispectral line scanners and associated custom- made complex optomechanical servomechanisms, sensors, readout circuitry, and packaging. Instead, the WASP system would be based on commercial off-the-shelf (COTS) equipment that would include (1) three or four electronic cameras (one for each of three or four wavelength bands) instead of a multispectral line scanner; (2) all associated drive and readout electronics; (3) a camera-pointing gimbal; (4) an inertial measurement unit (IMU) and a Global Positioning System (GPS) receiver for measuring the position, velocity, and orientation of the aircraft; and (5) a data-acquisition subsystem. It would be necessary to custom-develop an integrated sensor optical-bench assembly, a sensor-management subsystem, and software. The use of mostly COTS equipment is intended to reduce development time and cost, relative to those of prior systems.

McKeown, Donald↗

NASA NTRS2008

Autonomous Flight Safety System - Phase III

The Autonomous Flight Safety System (AFSS) is a joint KSC and Wallops Flight Facility project that uses tracking and attitude data from onboard Global Positioning System (GPS) and inertial measurement unit (IMU) sensors and configurable rule-based algorithms to make flight termination decisions. AFSS objectives are to increase launch capabilities by permitting launches from locations without range safety infrastructure, reduce costs by eliminating some downrange tracking and communication assets, and reduce the reaction time for flight termination decisions.

Source record↗

NASA NTRS2009

GPS/MEMS IMU/Microprocessor Board for Navigation

A miniaturized instrumentation package comprising a (1) Global Positioning System (GPS) receiver, (2) an inertial measurement unit (IMU) consisting largely of surface-micromachined sensors of the microelectromechanical systems (MEMS) type, and (3) a microprocessor, all residing on a single circuit board, is part of the navigation system of a compact robotic spacecraft intended to be released from a larger spacecraft [e.g., the International Space Station (ISS)] for exterior visual inspection of the larger spacecraft. Variants of the package may also be useful in terrestrial collision-detection and -avoidance applications. The navigation solution obtained by integrating the IMU outputs is fed back to a correlator in the GPS receiver to aid in tracking GPS signals. The raw GPS and IMU data are blended in a Kalman filter to obtain an optimal navigation solution, which can be supplemented by range and velocity data obtained by use of (l) a stereoscopic pair of electronic cameras aboard the robotic spacecraft and/or (2) a laser dynamic range imager aboard the ISS. The novelty of the package lies mostly in those aspects of the design of the MEMS IMU that pertain to controlling mechanical resonances and stabilizing scale factors and biases.

Gender, Thomas K.↗

NASA NTRS2009

Alignment Jig for the Precise Measurement of THz Radiation

Javadi, Hamid H.↗

NASA NTRS2009

Mars Entry Atmospheric Data System Modelling and Algorithm Development

The Mars Entry Atmospheric Data System (MEADS) is being developed as part of the Mars Science Laboratory (MSL), Entry, Descent, and Landing Instrumentation (MEDLI) project. The MEADS project involves installing an array of seven pressure transducers linked to ports on the MSL forebody to record the surface pressure distribution during atmospheric entry. These measured surface pressures are used to generate estimates of atmospheric quantities based on modeled surface pressure distributions. In particular, the quantities to be estimated from the MEADS pressure measurements include the total pressure, dynamic pressure, Mach number, angle of attack, and angle of sideslip. Secondary objectives are to estimate atmospheric winds by coupling the pressure measurements with the on-board Inertial Measurement Unit (IMU) data. This paper provides details of the algorithm development, MEADS system performance based on calibration, and uncertainty analysis for the aerodynamic and atmospheric quantities of interest. The work presented here is part of the MEDLI performance pre-flight validation and will culminate with processing flight data after Mars entry in 2012.

Karlgaard, Christopher D.↗

NASA NTRS2009

Apollo 13 Guidance, Navigation, and Control Challenges

Combustion and rupture of a liquid oxygen tank during the Apollo 13 mission provides lessons and insights for future spacecraft designers and operations personnel who may never, during their careers, have participated in saving a vehicle and crew during a spacecraft emergency. Guidance, Navigation, and Control (GNC) challenges were the reestablishment of attitude control after the oxygen tank incident, re-establishment of a free return trajectory, resolution of a ground tracking conflict between the LM and the Saturn V S-IVB stage, Inertial Measurement Unit (IMU) alignments, maneuvering to burn attitudes, attitude control during burns, and performing manual GNC tasks with most vehicle systems powered down. Debris illuminated by the Sun and gaseous venting from the Service Module (SM) complicated crew attempts to identify stars and prevented execution of nominal IMU alignment procedures. Sightings on the Sun, Moon, and Earth were used instead. Near continuous communications with Mission Control enabled the crew to quickly perform time critical procedures. Overcoming these challenges required the modification of existing contingency procedures.

Goodman, John L.↗

NASA NTRS2009

Enhanced Video-Oculography System

A previously developed video-oculography system has been enhanced for use in measuring vestibulo-ocular reflexes of a human subject in a centrifuge, motor vehicle, or other setting. The system as previously developed included a lightweight digital video camera mounted on goggles. The left eye was illuminated by an infrared light-emitting diode via a dichroic mirror, and the camera captured images of the left eye in infrared light. To extract eye-movement data, the digitized video images were processed by software running in a laptop computer. Eye movements were calibrated by having the subject view a target pattern, fixed with respect to the subject s head, generated by a goggle-mounted laser with a diffraction grating. The system as enhanced includes a second camera for imaging the scene from the subject s perspective, and two inertial measurement units (IMUs) for measuring linear accelerations and rates of rotation for computing head movements. One IMU is mounted on the goggles, the other on the centrifuge or vehicle frame. All eye-movement and head-motion data are time-stamped. In addition, the subject s point of regard is superimposed on each scene image to enable analysis of patterns of gaze in real time.

Moore, Steven T.↗

NASA NTRS2009

Estimating Thruster Impulses From IMU and Doppler Data

A computer program implements a thrust impulse measurement (TIM) filter, which processes data on changes in velocity and attitude of a spacecraft to estimate the small impulsive forces and torques exerted by the thrusters of the spacecraft reaction control system (RCS). The velocity-change data are obtained from line-of-sight-velocity data from Doppler measurements made from the Earth. The attitude-change data are the telemetered from an inertial measurement unit (IMU) aboard the spacecraft. The TIM filter estimates the threeaxis thrust vector for each RCS thruster, thereby enabling reduction of cumulative navigation error attributable to inaccurate prediction of thrust vectors. The filter has been augmented with a simple mathematical model to compensate for large temperature fluctuations in the spacecraft thruster catalyst bed in order to estimate thrust more accurately at deadbanding cold-firing levels. Also, rigorous consider-covariance estimation is applied in the TIM to account for the expected uncertainty in the moment of inertia and the location of the center of gravity of the spacecraft. The TIM filter was built with, and depends upon, a sigma-point consider-filter algorithm implemented in a Python-language computer program.

Lisano, Michael E.↗

NASA NTRS2009

Port-O-Sim Object Simulation Application

Port-O-Sim is a software application that supports engineering modeling and simulation of launch-range systems and subsystems, as well as the vehicles that operate on them. It is flexible, distributed, object-oriented, and realtime. A scripting language is used to configure an array of simulation objects and link them together. The script is contained in a text file, but executed and controlled using a graphical user interface. A set of modules is defined, each with input variables, output variables, and settings. These engineering models can be either linked to each other or run as standalone. The settings can be modified during execution. Since 2001, this application has been used for pre-mission failure mode training for many Range Safety Scenarios. It contains range asset link analysis, develops look-angle data, supports sky-screen site selection, drives GPS (Global Positioning System) and IMU (Inertial Measurement Unit) simulators, and can support conceptual design efforts for multiple flight programs with its capacity for rapid six-degrees-of-freedom model development. Due to the assembly of various object types into one application, the application is applicable across a wide variety of launch range problem domains.

Lanzi, Raymond J.↗

NASA NTRS2010

A Comparison Between Orion Automated and Space Shuttle Rendezvous Techniques

The Orion spacecraft will replace the space shuttle and will be the first human spacecraft since the Apollo program to leave low earth orbit. This vehicle will serve as the cornerstone of a complete space transportation system with a myriad of mission requirements necessitating rendezvous to multiple vehicles in earth orbit, around the moon and eventually beyond . These goals will require a complex and robust vehicle that is, significantly different from both the space shuttle and the command module of the Apollo program. Historically, orbit operations have been accomplished with heavy reliance on ground support and manual crew reconfiguration and monitoring. One major difference with Orion is that automation will be incorporated as a key element of the man-vehicle system. The automated system will consist of software devoted to transitioning between events based on a master timeline. This effectively adds a layer of high level sequencing that moves control of the vehicle from one phase to the next. This type of automated control is not entirely new to spacecraft since the shuttle uses a version of this during ascent and entry operations. During shuttle orbit operations however many of the software modes and hardware switches must be manually configured through the use of printed procedures and instructions voiced from the ground. The goal of the automation scheme on Orion is to extend high level automation to all flight phases. The move towards automation represents a large shift from current space shuttle operations, and so these new systems will be adopted gradually via various safeguards. These include features such as authority-to-proceed, manual down modes, and functional inhibits. This paper describes the contrast between the manual and ground approach of the space shuttle and the proposed automation of the Orion vehicle. I will introduce typical orbit operations that are common to all rendezvous missions and go on to describe the current Orion automation architecture and contrast it with shuttle rendezvous techniques and circumstances. The shuttle rendezvous profile is timed to take approximately 3 days from orbit insertion to docking at the International Space Station (ISS). This process can be divided into 3 phases: far-field, mid-field and proximity operations. The far-field stage is characterized as the most quiescent phase. The spacecraft is usually too far to navigate using relative sensors and uses the Inertial Measurement Units (IMU s) to numerically solve for its position. The maneuvers are infrequent, roughly twice per day, and are larger than other burns in the profile. The shuttle uses this opportunity to take extensive ground based radar updates and keep high fidelity orbit states on the ground. This state is then periodically uplinked to the shuttle computers. The targeting solutions for burn maneuvers are also computed on the ground and uplinked. During the burn the crew is responsible for setting the shuttle attitude and configuring the propulsion system for ignition. Again this entire process is manually driven by both crew and ground activity. The only automatic processes that occur are associated with the real-time execution of the burn. The Orion automated functionality will seek to relieve the workload of both the crew and ground during this phase

Ruiz, Jose O,↗

NASA NTRS2007

Robotic Vision-Based Localization in an Urban Environment

A system of electronic hardware and software, now undergoing development, automatically estimates the location of a robotic land vehicle in an urban environment using a somewhat imprecise map, which has been generated in advance from aerial imagery. This system does not utilize the Global Positioning System and does not include any odometry, inertial measurement units, or any other sensors except a stereoscopic pair of black-and-white digital video cameras mounted on the vehicle. Of course, the system also includes a computer running software that processes the video image data. The software consists mostly of three components corresponding to the three major image-data-processing functions: Visual Odometry This component automatically tracks point features in the imagery and computes the relative motion of the cameras between sequential image frames. This component incorporates a modified version of a visual-odometry algorithm originally published in 1989. The algorithm selects point features, performs multiresolution area-correlation computations to match the features in stereoscopic images, tracks the features through the sequence of images, and uses the tracking results to estimate the six-degree-of-freedom motion of the camera between consecutive stereoscopic pairs of images (see figure). Urban Feature Detection and Ranging Using the same data as those processed by the visual-odometry component, this component strives to determine the three-dimensional (3D) coordinates of vertical and horizontal lines that are likely to be parts of, or close to, the exterior surfaces of buildings. The basic sequence of processes performed by this component is the following: 1. An edge-detection algorithm is applied, yielding a set of linked lists of edge pixels, a horizontal-gradient image, and a vertical-gradient image. 2. Straight-line segments of edges are extracted from the linked lists generated in step 1. Any straight-line segments longer than an arbitrary threshold (e.g., 30 pixels) are assumed to belong to buildings or other artificial objects. 3. A gradient-filter algorithm is used to test straight-line segments longer than the threshold to determine whether they represent edges of natural or artificial objects. In somewhat oversimplified terms, the test is based on the assumption that the gradient of image intensity varies little along a segment that represents the edge of an artificial object.

Mchenry, Michael↗

NASA NTRS2010

Integration and Testing of the Lunar Reconnaissance Orbiter Attitude Control System

Throughout the Lunar Reconnaissance Orbiter (LRO) Integration and Testing (I&T) phase of the project, the Attitude Control System (ACS) team completed numerous tests on each hardware component in ever more flight like environments. The ACS utilizes a select group of attitude sensors and actuators. This paper chronicles the evolutionary steps taken to verify each component was constantly ready for flight as well as providing invaluable trending experience with the actual hardware. The paper includes a discussion of each ACS hardware component, lessons learned of the various stages of I&T, a discussion of the challenges that are unique to the LRO project, as well as a discussion of work for future missions to consider as part of their I&T plan. LRO ACS sensors were carefully installed, tested, and maintained over the 18 month I&T and prelaunch timeline. Care was taken with the optics of the Adcole Coarse Sun Sensors (CSS) to ensure their critical role in the Safe Hold mode was fulfilled. The use of new CSS stimulators provided the means of testing each CSS sensor independently, in ambient and vacuum conditions as well as over a wide range of thermal temperatures. Extreme bright light sources were also used to test the CSS in ambient conditions. The integration of the two SELEX Galileo Star Trackers was carefully planned and executed. Optical ground support equipment was designed and used often to check the performance of the star trackers throughout I&T in ambient and thermal/vacuum conditions. A late discovery of potential contamination of the star tracker light shades is discussed in this paper. This paper reviews how each time the spacecraft was at a new location and orientation, the Honeywell Miniature Inertial Measurement Unit (MIMU) was checked for data output validity. This gyro compassing test was performed at several key testing points in the timeline as well as several times while LRO was on the launch pad. Sensor alignment tests were completed several times to ensure that hardware remained on a rigid platform.

Simpson, Jim↗

NASA NTRS2010

Configurable Multi-Purpose Processor

Advancements in technology have allowed the miniaturization of systems used in aerospace vehicles. This technology is driven by the need for next-generation systems that provide reliable, responsive, and cost-effective range operations while providing increased capabilities such as simultaneous mission support, increased launch trajectories, improved launch, and landing opportunities, etc. Leveraging the newest technologies, the command and telemetry processor (CTP) concept provides for a compact, flexible, and integrated solution for flight command and telemetry systems and range systems. The CTP is a relatively small circuit board that serves as a processing platform for high dynamic, high vibration environments. The CTP can be reconfigured and reprogrammed, allowing it to be adapted for many different applications. The design is centered around a configurable field-programmable gate array (FPGA) device that contains numerous logic cells that can be used to implement traditional integrated circuits. The FPGA contains two PowerPC processors running the Vx-Works real-time operating system and are used to execute software programs specific to each application. The CTP was designed and developed specifically to provide telemetry functions; namely, the command processing, telemetry processing, and GPS metric tracking of a flight vehicle. However, it can be used as a general-purpose processor board to perform numerous functions implemented in either hardware or software using the FPGA s processors and/or logic cells. Functionally, the CTP was designed for range safety applications where it would ultimately become part of a vehicle s flight termination system. Consequently, the major functions of the CTP are to perform the forward link command processing, GPS metric tracking, return link telemetry data processing, error detection and correction, data encryption/ decryption, and initiate flight termination action commands. Also, the CTP had to be designed to survive and operate in a launch environment. Additionally, the CTP was designed to interface with the WFF (Wallops Flight Facility) custom-designed transceiver board which is used in the Low Cost TDRSS Transceiver (LCT2) also developed by WFF. The LCT2 s transceiver board demodulates commands received from the ground via the forward link and sends them to the CTP, where they are processed. The CTP inputs and processes data from the inertial measurement unit (IMU) and the GPS receiver board, generates status data, and then sends the data to the transceiver board where it is modulated and sent to the ground via the return link. Overall, the CTP has combined processing with the ability to interface to a GPS receiver, an IMU, and a pulse code modulation (PCM) communication link, while providing the capability to support common interfaces including Ethernet and serial interfaces boarding a relatively small-sized, lightweight package.

Valencia, J. Emilio↗

NASA NTRS2010

Demonstration of an Aerocapture GN and C System Through Hardware-in-the-Loop Simulations

Aerocapture is an orbit insertion maneuver in which a spacecraft flies through a planetary atmosphere one time using drag force to decelerate and effect a hyperbolic to elliptical orbit change. Aerocapture employs a feedback Guidance, Navigation, and Control (GN&C) system to deliver the spacecraft into a precise postatmospheric orbit despite the uncertainties inherent in planetary atmosphere knowledge, entry targeting and aerodynamic predictions. Only small amounts of propellant are required for attitude control and orbit adjustments, thereby providing mass savings of hundreds to thousands of kilograms over conventional all-propulsive techniques. The Analytic Predictor Corrector (APC) guidance algorithm has been developed to steer the vehicle through the aerocapture maneuver using bank angle control. Through funding provided by NASA's In-Space Propulsion Technology Program, the operation of an aerocapture GN&C system has been demonstrated in high-fidelity simulations that include real-time hardware in the loop, thus increasing the Technology Readiness Level (TRL) of aerocapture GN&C. First, a non-real-time (NRT), 6-DOF trajectory simulation was developed for the aerocapture trajectory. The simulation included vehicle dynamics, gravity model, atmosphere model, aerodynamics model, inertial measurement unit (IMU) model, attitude control thruster torque models, and GN&C algorithms (including the APC aerocapture guidance). The simulation used the vehicle and mission parameters from the ST-9 mission. A 2000 case Monte Carlo simulation was performed and results show an aerocapture success rate of greater than 99.7%, greater than 95% of total delta-V required for orbit insertion is provided by aerodynamic drag, and post-aerocapture orbit plane wedge angle error is less than 0.5 deg (3-sigma). Then a real-time (RT), 6-DOF simulation for the aerocapture trajectory was developed which demonstrated the guidance software executing on a flight-like computer, interfacing with a simulated IMU and simulated thrusters, with vehicle dynamics provided by an external simulator. Five cases from the NRT simulations were run in the RT simulation environment. The results compare well to those of the NRT simulation thus verifying the RT simulation configuration. The results of the above described simulations show the aerocapture maneuver using the APC algorithm can be accomplished reliably and the algorithm is now at TRL-6. Flight validation is the next step for aerocapture technology development.

Masciarelli, James↗

NASA NTRS2010

PhoneSat: Ground Testing of a Phone-Based Prototype Bus

Most of the key capabilities that are requisite of a satellite bus are housed in today's smart phones. PhoneSat refers to an initiative to build a ground-based prototype vehicle that could all the basic functionality of a satellite, including attitude control, using a smart Phone as its central hardware. All components used were also low cost Commercial off the Shelf (COTS). In summer 2009, an initial prototype was created using the LEGO Mindstorm toolkit demonstrating simple attitude control. Here we report on a follow up initiative to design, build and test a vehicle based on the Google s smart phone Nexus One. The report includes results from initial thermal-vacuum chamber tests and low altitude sub-orbital rocket flights which show that, at least for short durations, the Nexus One phone is able to withstand key aspects of the space environment without failure. We compare the sensor data from the Phone's accelerometers and magnetometers with that of an external microelectronic inertial measurement unit.

Felix, Carmen↗

NASA NTRS2011

Backup Alignment Devices on Shuttle: Heads-Up Display or Crew Optical Alignment Sight

NASA s Space Shuttle was built to withstand multiple failures while still keeping the crew and vehicle safe. Although the design of the Space Shuttle had a great deal of redundancy built into each system, there were often additional ways to keep systems in the best configuration if a failure were to occur. One such method was to use select pieces of hardware in a way for which they were not primarily intended. The primary function of the Heads-Up Display (HUD) was to provide the crew with a display of flight critical information during the entry phase. The primary function of the Crew Optical Alignment Sight (COAS) was to provide the crew an optical alignment capability for rendezvous and docking phases. An alignment device was required to keep the Inertial Measurement Units (IMUs) well aligned for a safe Entry; nominally this alignment device would be the two on-board Star Trackers. However, in the event of a Star Tracker failure, the HUD or COAS could also be used as a backup alignment device, but only if the device had been calibrated beforehand. Once the HUD or COAS was calibrated and verified then it was considered an adequate backup to the Star Trackers for entry IMU alignment. There were procedures in place and the astronauts were trained on how to accurately calibrate the HUD or COAS and how to use them as an alignment device. The calibration procedure for the HUD and COAS had been performed on many Shuttle missions. Many of the first calibrations performed were for data gathering purposes to determine which device was more accurate as a backup alignment device, HUD or COAS. Once this was determined, the following missions would frequently calibrate the HUD in order to be one step closer to having the device ready in case it was needed as a backup alignment device.

Chavez, Melissa A.↗

NASA NTRS2011

LIDAR-Aided Inertial Navigation with Extended Kalman Filtering for Pinpoint Landing

In support of NASA s Autonomous Landing and Hazard Avoidance Technology (ALHAT) project, an extended Kalman filter routine has been developed for estimating the position, velocity, and attitude of a spacecraft during the landing phase of a planetary mission. The proposed filter combines measurements of acceleration and angular velocity from an inertial measurement unit (IMU) with range and Doppler velocity observations from an onboard light detection and ranging (LIDAR) system. These high-precision LIDAR measurements of distance to the ground and approach velocity will enable both robotic and manned vehicles to land safely and precisely at scientifically interesting sites. The filter has been extensively tested using a lunar landing simulation and shown to improve navigation over flat surfaces or rough terrain. Experimental results from a helicopter flight test performed at NASA Dryden in August 2008 demonstrate that LIDAR can be employed to significantly improve navigation based exclusively on IMU integration.

Busnardo, David M.↗

NASA NTRS2011

Assessment of an Automated Touchdown Detection Algorithm for the Orion Crew Module

Orion Crew Module (CM) touchdown detection is critical to activating the post-landing sequence that safe?s the Reaction Control Jets (RCS), ensures that the vehicle remains upright, and establishes communication with recovery forces. In order to accommodate safe landing of an unmanned vehicle or incapacitated crew, an onboard automated detection system is required. An Orion-specific touchdown detection algorithm was developed and evaluated to differentiate landing events from in-flight events. The proposed method will be used to initiate post-landing cutting of the parachute riser lines, to prevent CM rollover, and to terminate RCS jet firing prior to submersion. The RCS jets continue to fire until touchdown to maintain proper CM orientation with respect to the flight path and to limit impact loads, but have potentially hazardous consequences if submerged while firing. The time available after impact to cut risers and initiate the CM Up-righting System (CMUS) is measured in minutes, whereas the time from touchdown to RCS jet submersion is a function of descent velocity, sea state conditions, and is often less than one second. Evaluation of the detection algorithms was performed for in-flight events (e.g. descent under chutes) using hi-fidelity rigid body analyses in the Decelerator Systems Simulation (DSS), whereas water impacts were simulated using a rigid finite element model of the Orion CM in LS-DYNA. Two touchdown detection algorithms were evaluated with various thresholds: Acceleration magnitude spike detection, and Accumulated velocity changed (over a given time window) spike detection. Data for both detection methods is acquired from an onboard Inertial Measurement Unit (IMU) sensor. The detection algorithms were tested with analytically generated in-flight and landing IMU data simulations. The acceleration spike detection proved to be faster while maintaining desired safety margin. Time to RCS jet submersion was predicted analytically across a series of simulated Orion landing conditions. This paper details the touchdown detection method chosen and the analysis used to support the decision.

Gay, Robert S.↗