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Robert A Williams

Publications and source records attributed to Robert A Williams.

3 records
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Precision Landing Navigation Performance of Human-Scale Lunar and Mars Landers

To meet the unique challenges of crewed Lunar and Mars precision landings, NASA’s Safe and Precise Landing Integrated Capabilities Evolution project has worked to advance autonomous spacecraft navigation by increasing the technology readiness level of key deorbit, entry, descent, and landing systems, including navigation sensors. Different sensors and their effects on overall system performance are evaluated using six-degree-of-freedom simulations with physics-based engineering models that capture the relevant vehicle systems and environmental effects. Building on an existing simulation framework, this work demonstrates how improved modeling fidelity enables rapid and detailed assessment of various navigation sensors on human-scale Lunar and Mars landing vehicles using NASA reference architectures.

navigation↗
NASA NTRSDate not supplied

Multi-Model Monte Carlo Estimators for Trajectory Simulation

Predicting landing radius and other quantities of interest (QoI) for entry, descent, andlanding (EDL) applications requires a viable uncertainty propagation method for quantifying the impact of uncertainties in aerodynamics, atmosphere, mass properties, etc. While standard Monte Carlo (MC) simulation is the de facto standard for producing robust and unbiasedstatistical estimators, it is often infeasible for expensive, high-fidelity models. Low-fidelity models are commonly constructed to replace the high-fidelity model in MC simulation for computational speedup, but at the expense of accuracy and unbiasedness. Emerging multi-model MC methods are bridging this gap by combining predictions from two or more modelsof varying fidelity and computational cost for efficient and unbiased uncertainty propagation.This works establishes a proof of concept for using multi-model MC to increase the speed and precision of trajectory simulation for EDL. It is shown that combining a high-fidelity EDL model with low-fidelity models (e.g., data-driven, reduced physics) in this manner has the potential to yield significant efficiency and accuracy gains for certain EDL QoIs versusa standard MC approach. Moreover, the unbiasedness of multi-model MC predictions ishighlighted by showing increased accuracy versus an approach that leverages a low-fidelity model alone.

James E Warner↗