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Knight, Russell

Publications and source records attributed to Knight, Russell.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

FY2024 Q4 L2 Milestone 8237 for Opacity-on-NIF

This document addresses parts 2 and 3 of the original milestone request. In the following writeup, the development of OpSpecTR at LLNL by LLNL and NNSS personnel will be detailed first, followed by a write-up of the development of Vme-resolved simulaVons of the opacity-on-NIF experiment. Capsule backlight simulaVons, hohlraum simulaVons, and sample simulaVons from CASSIO are combined to produce the line of sight from the capsule to an effecVve OpSpec posiVon. Spectra are generated by post-processing these simulaVons from either Spect3D, a commercial software by Prism ComputaVonal Sciences, or FESTR (Finite Element Spectroscopic Transport of RadiaVon), a LANL code. While the simulaVons do not at this stage proceed late enough to include the worst of the backgrounds generated from the hohlraum experimentally and each post-processing simulaVon includes only one ray for each spectrometer channel, these efforts represent proof of concept for a new capability to complete Vme-resolved and Vmegated simulaVons for the complete line of sight of the complex opacity-on-NIF geometry. Future work will be discussed at the end of the report for both efforts.

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NASA NTRS2018

Area Coverage Planning with 3-axis Steerable, 2D Framing Sensors

Existing algorithms for Agile Earth Observing Satellites((Lemaitre et al. 2002)) were largely created for 1D line sensors that acquire images in linear swaths. However, imaging satellites increasingly use 2D framing sensors (cameras) that capture discrete rectangular images. We describe tiling step-stare approaches that are more suited to rectangular image footprints than are 1D swath-based algorithms. Optimal area planning for these 2D framing instruments is an NPcomplete problem and intractable for large areas, so we present four approximation algorithms. Strategies are compared against a prior 2D framing instrument algorithm (Knight 2014) in three computational experiments. The impact of observer agility on schedule makespan is examined. Makespans vary more as observer agility decreases toward a critical point, then vary less after the critical point, suggesting a possible problem phase transition.

Chien, Steve↗
NASA NTRS2017

Heuristic Area Cost Estimation for Observational Coverage Schedulers

This paper presents a comparison of heuris- tics used to estimate the amount of time it would take for a spacecraft to image an area using Boustrophedon decomposition (Choset and Pignon 1998). Machine learning tech- niques are used to characterize algorithmic performance of coverage algorithms. It is shown that an ordinary least-squares linear model is among the most accurate in a set of constant and linear order regression models both in terms of memory consumption and schedule duration. These are demonstrated using the ASPEN planning system (Fukunaga et al. 1997) on the Eagle Eye domain.

Knight, Russell↗
NASA NTRS2017

A Hybrid Traveling Salesman Problem - Squeaky Wheel Optimization Planner for Earth Observational Scheduling

We outline a hybrid planner for scheduling Observa- tion Requests on an Earth observing satellite, subject to a variety of constraints for the ASPEN (Chien et al. 2000) Eagle Eye adaptation (Knight, Donnellan, and Green 2013) that combines Squeaky Wheel Optimiza- tion (Joslin and Clements 1999) with sliding observa- tion planning (Aldinger et al. 2013). The Earth Ob- serving Satellite (EOS) planning problem (Globus et al. 2004) is reformulated as time-varying travel time TSP with interval constraints (Ichoua, Gendreau, and Potvin 2003). The replanning/ ll stage of the hybrid scheduler marginally improves schedule quality for all bus agilities examined, but has more impact on lower agility observers. The squeaky wheel stage primarily a ects overall schedule quality by satisfying high pri- ority requests, while the replanner reduces starvation of lower value requests.

Trowbridge, Michael↗
NASA NTRS2014

Automated Scheduling of Personnel to Staff Operations for the Mars Science Laboratory

Leveraging previous work on scheduling personnel for space mission operations, we have adapted ASPEN (Activity Scheduling and Planning Environment) [1] to the domain of scheduling personnel for operations of the Mars Science Laboratory. Automated scheduling of personnel is not new. We compare our representations to a sampling of employee scheduling systems available with respect to desired features. We described the constraints required by MSL personnel schedulers and how each is handled by the scheduling algorithm.

MSL↗
NASA NTRS2013

Automating Stowage Operations for the International Space Station

A challenge for any proposed mission is to demonstrate convincingly that the proposed systems will in fact deliver the science promised. Funding agencies and mission design personnel are becoming ever more skeptical of the abstractions that form the basis of the current state of the practice with respect to approximating science return. To address this, we have been using automated planning and scheduling technology to provide actual coverage campaigns that provide better predictive performance with respect to science return for a given mission design and set of mission objectives given implementation uncertainties. Specifically, we have applied an adaptation of ASPEN and SPICE to the Eagle-Eye domain that demonstrates the performance of the mission design with respect to coverage of science imaging targets that address climate change and disaster response. Eagle-Eye is an Earth-imaging telescope that has been proposed to fly aboard the International Space Station (ISS).

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