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At least 703 records · Page 39

Use of Very Long Baseline Array Interferometric Data for Spacecraft Navigation

The main VLBI technique that is used at JPL is known as the Delta Differential One-way Ranging ((Delta)DOR). Two DSN antennas simultaneously track a source, and alternate between sources. The signals recorded at the antennas from each source are correlated to obtain the delay in arrival to the two antennas, and the delays are differenced to remove common-source errors. An alternative technique is to use carrier phase differences between antennas. This is routinely done by the Very Large Baseline Array (VLBA) as part of source imaging. The VLBA capabilities are used for scientific research, but also have the potential to be used for navigation. Two main experiments were performed with the VLBA and JPL spacecraft. This paper describes and analyzes these experiments and discusses the possible uses of VLBA tracking for spacecraft navigation.

Very Large Baseline Array (VLBA)↗

AutoNav Mark3: Engineering the Next Generation of Autonomous Onboard Navigation and Guidance

The success of JPL's AutoNav system at comet Tempel-1 on July 4, 2005, demonstrated the power of autonomous navigation technology for the Deep Impact Mission. This software is being planned for use as the onboard navigation, tracking and rendezvous system for a Mars Sample Return Mission technology demonstration, and several mission proposals are evaluating its use for rendezvous with, and landing on asteroids. Before this however, extensive re-engineering of AutoNav will take place. This paper describes the AutoNav systems-engineering effort in several areas: extending the capabilities, improving operability, utilizing new hardware elements, and demonstrating the new possibilities of AutoNav in simulations.

navigation↗

Earth-Like Exoplanets: The Science of NASA's Navigator Program

This book outlines the exoplanet science content of NASA's Navigator Program, and it identifies the exoplanet research priorities. The goal of Navigator Program missions is to detect and characterize Earth-like planets in the habitable zone of nearby stars and to search for signs of life on those planets.

habitable planets↗

Real Time Mars Approach Navigation Aided by the Mars Network

A NASA Mars technology project is described that is building a prototype embedded real time Mars approach navigation capability which can be hosted on the Mars Network's Electra transceiver. The paper motivates the reason for doing real time Mars approach navigation via a set of analyses demonstrating its utility for enabling Mars pin-point landing (less than 1-km landing error). The development approach, software design, and test results are discussed. Finally, the way forward towards a flight demonstration on the Mars Science Laboratory (MSL) is presented.

Mars Network↗

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↗

Learning for Autonomous Navigation

Robotic ground vehicles for outdoor applications have achieved some remarkable successes, notably in autonomous highway following (Dickmanns, 1987), planetary exploration (1), and off-road navigation on Earth (1). Nevertheless, major challenges remain to enable reliable, high-speed, autonomous navigation in a wide variety of complex, off-road terrain. 3-D perception of terrain geometry with imaging range sensors is the mainstay of off-road driving systems. However, the stopping distance at high speed exceeds the effective lookahead distance of existing range sensors. Prospects for extending the range of 3-D sensors is strongly limited by sensor physics, eye safety of lasers, and related issues. Range sensor limitations also allow vehicles to enter large cul-de-sacs even at low speed, leading to long detours. Moreover, sensing only terrain geometry fails to reveal mechanical properties of terrain that are critical to assessing its traversability, such as potential for slippage, sinkage, and the degree of compliance of potential obstacles. Rovers in the Mars Exploration Rover (MER) mission have got stuck in sand dunes and experienced significant downhill slippage in the vicinity of large rock hazards. Earth-based off-road robots today have very limited ability to discriminate traversable vegetation from non-traversable vegetation or rough ground. It is impossible today to preprogram a system with knowledge of these properties for all types of terrain and weather conditions that might be encountered.

mixture of Gaussians↗

Mars Science Laboratory Interplanetary Navigation Analysis

The Mars Science Laboratory (MSL) is a NASA rover mission that will be launched in late 2011 and will land on Mars in August of 2012. This paper describes the analyses performed to validate the navigation system for launch, interplanetary cruise, and approach. MSL will use guidance during its descent into Mars in order to minimize landing dispersions, and therefore will be able to use smaller landing zones that are closer to terrain of high scientific interest. This will require a more accurate delivery of the spacecraft to the atmospheric entry interface, and a late update of the state of the spacecraft at entry. During cruise and approach the spacecraft may perform up to six trajectory correction maneuvers (TCMs), to target to the desired landing site with the required flight path angle at entry. Approach orbit determination covariance analyses have been performed to evaluate the accuracy that can be achieved in delivering the spacecraft to the entry interface point, and to determine how accurately the state of the spacecraft can be predicted to initialize the guidance algorithm. In addition, a sensitivity analysis has been performed to evaluate which factors most contribute to the improvement or degradation of the navigation performance, for both entry flight path angle delivery and entry state knowledge.

orbit determination↗

Methods of Optical Navigation

Optical navigation is the use of onboard imaging to aid in the determination of the spacecraft trajectory and of the targets' ephemerides. Opnav techniques provide a direct measurement of the direction from a spacecraft to target bodies. Opnav data thus complement both radiometric tracking data (for instance, Doppler and range) and the groundbased astrometry which is used to determine the a priori ephemeris of the targets. We present the geometry and camera models which form the mathematical basis for optical navigation and some of the image processing techniques by which one can extract the optical observables--that is, the sample and line coordinates of images--from pictures.

spacecraft navigation↗

Optical navigation planning process for the Cassini Solstice Mission

During the Cassini Equinox Mission, the Optical Navigation strategy has gradually evolved toward maintenance of an acceptable level of uncertainty on the positions of the bodies to be observed. By counteracting the runoff of the uncertainty over time, this strategy helps satisfy the spacecraft pointing requirements throughout the Solstice Mission, while considerably reducing the required imaging frequency. Requirements for planning observations were established, and the planning process itself was largely automated to facilitate re-planning if it becomes necessary. This paper summarizes the process leading to the optical navigation schedule for the seven years of the Solstice Mission.

Cassini↗

Engineering a Multimission Approach to Navigation Ground Data System Operations

The Mission Design and Navigation (MDNAV) Section at the Jet Propulsion Laboratory (JPL) supports many deep space and earth orbiting missions from formulation to end of mission operations. The requirements of these missions are met with a multimission approach to MDNAV ground data system (GDS) infrastructure capable of being shared and allocated in a seamless and consistent manner across missions. The MDNAV computing infrastructure consists of compute clusters, network attached storage, mission support area facilities, and desktop hardware. The multimission architecture allows these assets, and even personnel, to be leveraged effectively across the project lifecycle and across multiple missions simultaneously. It provides a more robust and capable infrastructure to each mission than might be possible if each constructed its own. It also enables a consistent interface and environment within which teams can conduct all mission analysis and navigation functions including: trajectory design; ephemeris generation; orbit determination; maneuver design; and entry, descent, and landing analysis. The savings of these efficiencies more than offset the costs of increased complexity and other challenges that had to be addressed: configuration management, scheduling conflicts, and competition for resources. This paper examines the benefits of the multimission MDNAV ground data system infrastructure, focusing on the hardware and software architecture. The result is an efficient, robust, scalable MDNAV ground data system capable of supporting more than a dozen active missions at once.

Mission Design and Navigation (MDNAV)↗

LiAISON-Supplemented Navigation for Geosynchronous and Lunar L1 Orbiter

A high-fidelity simulation is developed to test the theory that relative range and range-rate measurements between a satellite in a geosynchronous orbit and a satellite in orbit about the Earth-Moon L1 point may be used to improve the navigation accuracy of both satellites, compared with ground-only tracking. Recent research has determined that satellite-to-satellite range measurements between these two orbits are su cient to perform absolute state estimation as well as relative state estimation of both satellites - a technology known as LiAISON (Linked Autonomous Interplanetary Satellite Orbit Navigation). This study quanties the improvement in accuracy when LiAISON is used to supplement ground tracking of one or both satellites in a very realistic simulation. Orbit determination techniques show that the inclusion of LiAISON measurements with ground tracking observations can greatly improve absolute position and velocity estimates for both satellites in geosynchronous orbit and about the Earth-Moon L1 point.

satellite-to-satellite tracking (SST)↗

Raven: An On-Orbit Relative Navigation Demonstration Using International Space Station Visiting Vehicles

Since the last Hubble Servicing Mission five years ago, the Satellite Servicing Capabilities Office (SSCO) at the NASA Goddard Space Flight Center (GSFC) has been focusing on maturing the technologies necessary to robotically service orbiting legacy assets-spacecraft not necessarily designed for in-flight service. Raven, SSCO's next orbital experiment to the International Space Station (ISS), is a real-time autonomous non-cooperative relative navigation system that will mature the estimation algorithms required for rendezvous and proximity operations for a satellite-servicing mission. Raven will fly as a hosted payload as part of the Space Test Program's STP-H5 mission, which will be mounted on an external ExPRESS Logistics Carrier (ELC) and will image the many visiting vehicles arriving and departing from the ISS as targets for observation. Raven will host multiple sensors: a visible camera with a variable field of view lens, a long-wave infrared camera, and a short-wave flash lidar. This sensor suite can be pointed via a two-axis gimbal to provide a wide field of regard to track the visiting vehicles as they make their approach. Various real-time vision processing algorithms will produce range, bearing, and six degree of freedom pose measurements that will be processed in a relative navigation filter to produce an optimal relative state estimate. In this overview paper, we will cover top-level requirements, experimental concept of operations, system design, and the status of Raven integration and test activities.

Rendezvous↗

Navigation Challenges in the MAVEN Science Phase

The MAVEN spacecraft will explore Mars' upper atmosphere. The primary science phase will last one (Earth) year, during which the spacecraft will be in an elliptical 4.5 hour orbit at an inclination of 75 degrees. The 75 degree inclination results in the orbit periapsis oscillating between +/-75 degrees latitude, thus naturally covering most Mars latitudes during the primary mission. The orbit will be controlled via maneuvers so that the maximum orbit density remains in a density corridor. This results in the MAVEN science phase being in a light aerobraking type orbit of around 160 km for an extended period. In addition, the mission has significantly less tracking data than aerobraking phases of other missions, and even less than other NASA Mars orbiter primary phases. This results in significant challenges for the Navigation Team. They can be summarized as a difficulty in determining the current density profile, which maps into degraded trajectory predictions and less accurate control over the spacecraft location in the targeted density corridor via maneuvers. This paper describes these challenges and the Navigation Team's plans to meet them.

MAVEN↗

Mars Science Laboratory Navigation Results

The Mars Science Laboratory (MSL), carrying the Curiosity rover to Mars, was launched on November 26, 2011, from Cape Canaveral, Florida. The target for MSL was selected to be Gale Crater, near the equator of Mars, with an arrival date in early August 2012. The two main interplanetary navigation tasks for the mission were to deliver the spacecraft to an entry interface point that would allow the rover to safely reach the landing area, and to tell the spacecraft where it entered the atmosphere of Mars, so it could guide itself accurately to close proximity of the landing target. MSL used entry guidance as it slowed down from the entry speed to a speed low enough to allow for a successful parachute deployment, and this guidance allowed shrinking the landing ellipse to a 99% conservative estimate of 7 by 20 kilometers. Since there is no global positioning system in Mars, achieving this accuracy was predicated on flying a trajectory that closely matched the reference trajectory used to design the guidance algorithm, and on initializing the guidance system with an accurate Mars-relative entry state that could be used as the starting point to integrate the inertial measurement unit data during entry and descent. The pre-launch entry flight path angle (EFPA) delivery requirement was +/- 0.20 deg, but after launch a smaller threshold of +/- 0.05 deg was used as the criteria for late trajectory correction maneuver (TCM) decisions. The pre-launch requirement for entry state knowledge was 2.8 kilometers in position error and 2 meters per second in velocity error, but also smaller thresholds were defined after launch to evaluate entry state update opportunities. The biggest challenge for the navigation team was to accurately predict the trajectory of the spacecraft, so the estimates of the entry conditions could be stable, and late trajectory correction maneuvers or entry parameter updates could be waved off. As a matter of fact, the prediction accuracy was such that the last TCM performed was a small burn executed eight days before landing, and the entry state that was calculated just 36 hours after that TCM, and that was uploaded to the spacecraft the same day, did not need to be updated. The final EFPA was 0.013 deg shallower than the -15.5 deg target, and the on-board entry state was just 200 meters in position and 0.11 meters per second in velocity from the post-landing reconstructed entry state. Overall the entry delivery and knowledge requirements were fulfilled with a margin of more than 90% with respect to the pre-launch thresholds. This excellent accuracy contributed to a very successful and accurate entry, descent, and landing, and surface mission.

navigation↗

Safe Maritime Navigation with COLREGS Using Velocity Obstacles

This paper presents a motion planning algorithm for Unmanned Surface Vehicles (USVs) to navigate safely in dynamic, cluttered environments. The proposed algorithm not only addresses Hazard Avoidance (HA) for stationary and moving hazards but also applies the International Regulations for Preventing Collisions at Sea (known as COLREGs). The COLREG rules specify, for example, which vessel is responsible for giving way to the other and to which side of the "stand-on" vessel to maneuver. The three primary COLREG rules were considered in this paper: crossing, overtaking, and head-on situations. For USVs to be safely deployed in environments with other traffic boats, it is imperative that the USV's navigation algorithm obey COLREGs. Note also that if other boats disregard their responsibility under COLREGs, the USV will still apply its HA algorithms to avoid a collision. The proposed approach is based on Velocity Obstacles, which generates a cone-shaped obstacle in the velocity space. Because Velocity Obstacles also specify which side of the obstacle the vehicle will pass during the avoidance maneuver, COLREGs are encoded in the velocity space in a natural way. The algorithm is demonstrated via both simulation and on-water tests.

velocity obstacles↗

Navigation of the EPOXI Spacecraft to Comet Hartley 2

On November 4, 2010, the EPOXI spacecraft flew by the comet Hartley 2, marking the fourth time that a NASA spacecraft successfully captured high resolution images of a cometary nucleus. EPOXI is the extended mission of the Deep Impact mission, which delivered an impactor on comet Tempel-1 on July 4, 2005. EPOXI officially started in September 2007 and eventually took over 3 years of flight time and had 3 Earth gravity assists to achieve the proper encounter conditions. In the process, the mission was redesigned to accommodate a new comet as the target and changes in the trajectory to achieve better imaging conditions at encounter. Challenges in navigation of the spacecraft included precision targeting of several Earth flybys and the comet encounter, uncertainties in determining the ephemeris of the comet relative to the spacecraft, and the high accuracy trajectory knowledge needed to image the comet during the encounter. This paper presents an overview of the navigation process used for the mission.

Deep Impact (DI)↗

Performance Characterization of a Landmark Measurement System for ARRM Terrain Relative Navigation

This paper describes the landmark measurement system being developed for terrain relative navigation on NASAs Asteroid Redirect Robotic Mission (ARRM),and the results of a performance characterization study given realistic navigational and model errors. The system is called Retina, and is derived from the stereo-photoclinometry methods widely used on other small-body missions. The system is simulated using synthetic imagery of the asteroid surface and discussion is given on various algorithmic design choices. Unlike other missions, ARRMs Retina is the first planned autonomous use of these methods during the close-proximity and descent phase of the mission.

Guidance↗

Performance Characterization of a Landmark Measurement System for ARRM Terrain Relative Navigation

This paper describes the landmark measurement system being developed for terrain relative navigation on NASAs Asteroid Redirect Robotic Mission (ARRM),and the results of a performance characterization study given realistic navigational and model errors. The system is called Retina, and is derived from the stereophotoclinometry methods widely used on other small-body missions. The system is simulated using synthetic imagery of the asteroid surface and discussion is given on various algorithmic design choices. Unlike other missions, ARRMs Retina is the first planned autonomous use of these methods during the close-proximity and descent phase of the mission.

Navigation↗