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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Vehicle Detection for RCTA/ANS (Autonomous Navigation System)

Using a stereo camera pair, imagery is acquired and processed through the JPLV stereo processing pipeline. From this stereo data, large 3D blobs are found. These blobs are then described and classified by their shape to determine which are vehicles and which are not. Prior vehicle detection algorithms are either targeted to specific domains, such as following lead cars, or are intensity- based methods that involve learning typical vehicle appearances from a large corpus of training data. In order to detect vehicles, the JPL Vehicle Detection (JVD) algorithm goes through the following steps: 1. Take as input a left disparity image and left rectified image from JPLV stereo. 2. Project the disparity data onto a two-dimensional Cartesian map. 3. Perform some post-processing of the map built in the previous step in order to clean it up. 4. Take the processed map and find peaks. For each peak, grow it out into a map blob. These map blobs represent large, roughly vehicle-sized objects in the scene. 5. Take these map blobs and reject those that do not meet certain criteria. Build descriptors for the ones that remain. Pass these descriptors onto a classifier, which determines if the blob is a vehicle or not. The probability of detection is the probability that if a vehicle is present in the image, is visible, and un-occluded, then it will be detected by the JVD algorithm. In order to estimate this probability, eight sequences were ground-truthed from the RCTA (Robotics Collaborative Technology Alliances) program, totaling over 4,000 frames with 15 unique vehicles. Since these vehicles were observed at varying ranges, one is able to find the probability of detection as a function of range. At the time of this reporting, the JVD algorithm was tuned to perform best at cars seen from the front, rear, or either side, and perform poorly on vehicles seen from oblique angles.

Brennan, Shane↗
NASA NTRS2013

Cloud Absorption Radiometer Autonomous Navigation System - CANS

CAR (cloud absorption radiometer) acquires spatial reference data from host aircraft navigation systems. This poses various problems during CAR data reduction, including navigation data format, accuracy of position data, accuracy of airframe inertial data, and navigation data rate. Incorporating its own navigation system, which included GPS (Global Positioning System), roll axis inertia and rates, and three axis acceleration, CANS expedites data reduction and increases the accuracy of the CAR end data product. CANS provides a self-contained navigation system for the CAR, using inertial reference and GPS positional information. The intent of the software application was to correct the sensor with respect to aircraft roll in real time based upon inputs from a precision navigation sensor. In addition, the navigation information (including GPS position), attitude data, and sensor position details are all streamed to a remote system for recording and later analysis. CANS comprises a commercially available inertial navigation system with integral GPS capability (Attitude Heading Reference System AHRS) integrated into the CAR support structure and data system. The unit is attached to the bottom of the tripod support structure. The related GPS antenna is located on the P-3 radome immediately above the CAR. The AHRS unit provides a RS-232 data stream containing global position and inertial attitude and velocity data to the CAR, which is recorded concurrently with the CAR data. This independence from aircraft navigation input provides for position and inertial state data that accounts for very small changes in aircraft attitude and position, sensed at the CAR location as opposed to aircraft state sensors typically installed close to the aircraft center of gravity. More accurate positional data enables quicker CAR data reduction with better resolution. The CANS software operates in two modes: initialization/calibration and operational. In the initialization/calibration mode, the software aligns the precision navigation sensors and initializes the communications interfaces with the sensor and the remote computing system. It also monitors the navigation data state for quality and ensures that the system maintains the required fidelity for attitude and positional information. In the operational mode, the software runs at 12.5 Hz and gathers the required navigation/attitude data, computes the required sensor correction values, and then commands the sensor to the required roll correction. In this manner, the sensor will stay very near to vertical at all times, greatly improving the resulting collected data and imagery. CANS greatly improves quality of resulting imagery and data collected. In addition, the software component of the system outputs a concisely formatted, high-speed data stream that can be used for further science data processing. This precision, time-stamped data also can benefit other instruments on the same aircraft platform by providing extra information from the mission flight.

Kahle, Duncan↗
NASA NTRS2015

Crew-Aided Autonomous Navigation Project

Manual capability to perform star/planet-limb sightings provides a cheap, simple, and robust backup navigation source for exploration missions independent from the ground. Sextant sightings from spacecraft were first exercised in Gemini and flew as the loss-of-communications backup for all Apollo missions. This study seeks to procure and characterize error sources of navigation-grade sextants for feasibility of taking star and planetary limb sightings from inside a spacecraft. A series of similar studies was performed in the early/mid-1960s in preparation for Apollo missions, and one goal of this study is to modernize and update those findings. This technique has the potential to deliver significant risk mitigation, validation, and backup to more complex low-TRL automated systems under development involving cameras.

Holt, Greg↗
NASA NTRS2015

Crew-Aided Autonomous Navigation

A sextant provides manual capability to perform star/planet-limb sightings and offers a cheap, simple, robust backup navigation source for exploration missions independent from the ground. Sextant sightings from spacecraft were first exercised in Gemini and flew as the lost-communication backup for all Apollo missions. This study characterized error sources of navigation-grade sextants for feasibility of taking star and planetary limb sightings from inside a spacecraft. A series of similar studies was performed in the early/mid-1960s in preparation for Apollo missions. This study modernized and updated those findings in addition to showing feasibility using Linear Covariance analysis techniques. The human eyeball is a remarkable piece of optical equipment and provides many advantages over camera-based systems, including dynamic range and detail resolution. This technique utilizes those advantages and provides important autonomy to the crew in the event of lost communication with the ground. It can also provide confidence and verification of low-TRL automated onboard systems. The technique is extremely flexible and is not dependent on any particular vehicle type. The investigation involved procuring navigation-grade sextants and characterizing their performance under a variety of conditions encountered in exploration missions. The JSC optical sensor lab and Orion mockup were the primary testing locations. For the accuracy assessment, a group of test subjects took sextant readings on calibrated targets while instrument/operator precision was measured. The study demonstrated repeatability of star/planet-limb sightings with bias and standard deviation around 10 arcseconds, then used high-fidelity simulations to verify those accuracy levels met the needs for targeting mid-course maneuvers in preparation for Earth reen.

Holt, Greg N.↗
NASA NTRS2024

Autonomous Navigation of a Lunar Relay Using GNSS and Other Measurements

NASA’s Lunar Communications Relay and Navigation Services (LCRNS) project will establish a relay constellation at the Moon to provide the south pole region with communications and position, navigation, and time (PNT) services. These services will require highly accurate knowledge of position, velocity, and time (PVT) for each relay. This paper explores one approach for performing onboard PVT estimation, the LCRNS PNT Instrument (LPI). This paper considers different configurations of the instrument, specifically different measurement types and clocks, and the resulting navigation performance. Simulation results are first shown for an instrument configuration that uses GPS pseudorange and Doppler measurements with a highly sensitive GPS receiver (i.e., an acquisition and tracking threshold of 23 dB-Hz) and a chip-scale atomic clock (CSAC). The importance of Doppler, clock quality, receiver sensitivity, and optical navigation is examined through comparison of these results to other instrument configurations. Clock quality (i.e., stability) is the strongest determinant of achievable performance, and the inclusion of GPS Doppleralso has a significant effect. Finally, simulation results are compared to two laboratory tests: first a case that includes GPS receiver hardware in the loop, then a case that includes optical navigation and filter flight software in the loop. These agreeclosely with the simulation results and provide evidence the simulation is accurately modeling the instrument under development.

Benjamin W Ashman↗
NASA NTRS1991

Autonomous rough terrain navigation - Lessons learned

Because of light-time delays, a planetary rover located on Mars or beyond will probably need to be able to navigate autonomously, in order to do significant exploration. Recent work at JPL has explored several different autonomous navigation strategies. This work includes highly deliberative methods that require large amounts of computation and internal storage but yield very planful behavior to more reactive systems that require less resources but whose behavior is more difficult to model. This paper briefly presents these methods, the results from experiments both in simulation and in the field, and some conclusions on the value of different approaches, and their possible impact on the structure of the robot and the mission architecture.

Miller, David P.↗