The new JPL Small-Body Automatic Mission-Design System comprises two main elements: a database of pre-computed mission options to all known asteroids and comets, and an interactive web interface that can be used to design transfers to each small body. The system is kept current with the JPL Small Body orbit catalog, is publicly available, and can be accessed from the JPL Solar System Dynamics Group website. The missions computed by the automatic system are impulsive. However, a low-thrust v estimate is also provided. The database of pre-computed missions can be filtered to find potential targets with certain orbital and physical properties, and that meet specific mission-design constraints. In addition to describing the system in detail, this paper presents empirical analytic expressions to approximate the impulsive v requirements of missions to comets and to each family of asteroids, obtained by fitting statistical data. To show how the interactive interface works, we consider mission options to asteroid 99942 Apophis. We also include optimal mission opportunities to other selected small bodies.

Farnocchia, Davide↗

NASA NTRS2017

Semi-analytic preliminary design of low-thrust missions

Using generalized logarithmic spirals to approximate low-thrust trajectories, a new strategy for the design of low-thrust gravity-assist transfers has been developed. Each transfer leg is defined by a semi-analytic model, and its solution is equivalent to a hybrid Lambert’s problem. The method is suitable for approximating both flyby and rendezvous transfer legs. A branch and prune algorithm is used to generate a collection of initial guesses for further optimization. The analytic nature of the low-thrust model simplifies the pruning step, since dynamical and operational constraints (like maximum thrust or total v) can be imposed easily. The solutions obtained with the global search algorithm can be post-processed, filtered, and ranked according to various criteria. This is where the versatility of the method resides, because changing the selection criteria does not require a new search. Selected candidates are then optimized further, in order to generate actual low-thrust orbits. Two mission design examples are presented: an asteroid deflection mission using a kinetic impactor, and a rendezvous mission to Jupiter. These examples are used to analyze the convergence of the optimization stage, in particular how far from the optimal solution the initial guesses are.

Park, Ryan S.↗

NASA NTRS2017

Semi-analytic preliminary design of low-thrust missions

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Park, Ryan S.↗

NASA NTRS2017

Constraints on Ceres' internal structure from the Dawn gravity and shape data

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Zuber, Maria T.↗

NASA NTRS2016

Gravity Field of the Orientale Basin from the Gravity Recovery and Interior Laboratory Mission

The Orientale basin is the youngest and best-preserved major impact structure on the Moon. We used the Gravity Recovery and Interior Laboratory (GRAIL) spacecraft to investigate the gravitational field of Orientale at 3- to 5-kilometer (km) horizontal resolution. A volume of at least (3.4 +/- 0.2) × 10(exp 6) cu km of crustal material was removed and redistributed during basin formation. There is no preserved evidence of the transient crater that would reveal the basin's maximum volume, but its diameter may now be inferred to be between 320 and 460 km. The gravity field resolves distinctive structures of Orientale's three rings and suggests the presence of faults associated with the outer two that penetrate to the mantle. The crustal structure of Orientale provides constraints on the formation of multiring basins.

Zuber, Maria T.↗

NASA NTRS2016

The Deep-Space Positioning System Concept: Automating Complex Navigation Operations Beyond the Earth

There is no equivalent of GPS in “deep space,” that is beyond the Earth Moon system (and use of GPS beyond LEO and MEO is already problematic.) NASA has been awarded a patent on the concept of the “Deep-space Positioning System” (DPS) which is a self-contained instrument concept that would use in-situ observations to provide an automated and autonomous means of determining its position in the Solar System, and at least as important, its position relative to a target, whether that target is a planet, asteroid, moon or comet. In addition, the DPS system computes trajectory change parameters, as necessary to keep a spacecraft on a planned trajectory. Implementation options include the use of a radio receiver to receive a frequency beacon from a non-in-situ source for additional navigation information over and above that which can be provided by the cameras that are the principal data source of DPS. This paper discusses the DPS instrument concept, and its potential use in deep space.

Guinn, Joseph R.↗

NASA NTRS2013

Preliminary Results on Lunar Interior Properties from the GRAIL Mission

The Gravity Recovery and Interior Laboratory (GRAIL) mission has provided lunar gravity with unprecedented accuracy and resolution. GRAIL has produced a high-resolution map of the lunar gravity field while also determining tidal response. We present the latest gravity field solution and its preliminary implications for the Moon's interior structure, exploring properties such as the mean density, moment of inertia of the solid Moon, and tidal potential Love number k2. Lunar structure includes a thin crust, a deep mantle, a fluid core, and a suspected solid inner core. An accurate Love number mainly improves knowledge of the fluid core and deep mantle. In the future GRAIL will search for evidence of tidal dissipation and a solid inner core.

Williams, James G.↗

NASA NTRS2013

Gravity Recovery and Interior Laboratory (GRAIL): Extended Mission and End-Game Status

The Gravity Recovery and Interior Laboratory (GRAIL) [1], NASA s eleventh Discovery mission, successfully executed its Primary Mission (PM) in lunar orbit between March 1, 2012 and May 29, 2012. GRAIL s Extended Mission (XM) initiated on August 30, 2012 and was successfully completed on December 14, 2012. The XM provided an additional three months of gravity mapping at half the altitude (23 km) of the PM (55 km), and is providing higherresolution gravity models that are being used to map the upper crust of the Moon in unprecedented detail.

Zuber, Maria T.↗

NASA NTRS2013

Properties of the Lunar Interior: Preliminary Results from the GRAIL Mission

The Gravity Recovery and Interior Laboratory (GRAIL) mission [1] has provided lunar gravity with unprecedented accuracy and resolution. GRAIL has produced a high-resolution map of the lunar gravity field [2,3] while also determining tidal response. We present the latest gravity field solution and its preliminary implications for the Moon's interior structure, exploring properties such as the mean density, moment of inertia of the solid Moon, and tidal potential Love number k(sub 2). Lunar structure includes a thin crust, a thick mantle layer, a fluid outer core, and a suspected solid inner core. An accurate Love number mainly improves knowledge of the fluid core and deep mantle. In the future, we will search for evidence of tidal dissipation and a solid inner core using GRAIL data.

moon gravity↗

NASA NTRS2012

Spacecraft Thermal and Optical Modeling Impacts on Estimation of the GRAIL Lunar Gravity Field

We summarize work performed involving thermo-optical modeling of the two Gravity Recovery And Interior Laboratory (GRAIL) spacecraft. We derived several reconciled spacecraft thermo-optical models having varying detail. We used the simplest in calculating SRP acceleration, and used the most detailed to calculate acceleration due to thermal re-radiation. For the latter, we used both the output of pre-launch finite-element-based thermal simulations and downlinked temperature sensor telemetry. The estimation process to recover the lunar gravity field utilizes both a nominal thermal re-radiation accleration history and an apriori error model derived from that plus an off-nominal history, which bounds parameter uncertainties as informed by sensitivity studies.

thermal modeling↗

NASA NTRS2009

Trajectory Reconstruction of the ST-9 Sounding Rocket Experiment Using IMU and Landmark Data

This paper presents trajectory reconstruction of the ST-9 sounding rocket experiment using the onboard IMU data and descent imagery. The raw IMU accelerometer measurements are first converted into inertial acceleration and then used in trajectory integration. The descent images are pre-processed using a map-matching algorithm and unique landmarks for each image are created. Using the converted IMU data and descent images, the result from dead-reckoning and the kinematic-fix approaches are first compared with the GPS measurements. Then, both the IMU data and landmarks are processed together using a batch least-squares filter and the position, velocity, stochastic acceleration, and camera orientation of each image are estimated. The reconstructed trajectory is compared with the GPS data and the corresponding formal uncertainties are presented. The result shows that IMU data and descent images processed with a batch filter algorithm provide the trajectory accuracy required for pin-point landing.

Park, Ryan S.↗

NASA NTRS2008

Estimating Small-Body Gravity Field from Shape Model and Navigation Data

This paper presents a method to model the external gravity field and to estimate the internal density variation of a small-body. We first discuss the modeling problem, where we assume the polyhedral shape and internal density distribution are given, and model the body interior using finite elements definitions, such as cubes and spheres. The gravitational attractions computed from these approaches are compared with the true uniform-density polyhedral attraction and the level of accuracies are presented. We then discuss the inverse problem where we assume the body shape, radiometric measurements, and a priori density constraints are given, and estimate the internal density variation by estimating the density of each finite element. The result shows that the accuracy of the estimated density variation can be significantly improved depending on the orbit altitude, finite-element resolution, and measurement accuracy.

Covariance Analysis↗