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Title: Dynamic Multi-Sensor Multi-Mission Optimal Planning Tool

Abstract

Remote sensing systems have firmly established a role in providing immense value to commercial industry, scientific exploration, and national security. Continued maturation of sensing technology has reduced the cost of deploying highly-capable sensors while at the same time increased reliance on the information these sensors can provide. The demand for time on these sensors is unlikely to diminish. Coordination of next-generation sensor systems, larger constellations of satellites, unmanned aerial vehicles, ground telescopes, etc. is prohibitively complex for existing heuristicsbased scheduling techniques. The project was a two-year collaboration spanning multiple Sandia centers and included a partnership with Texas A&M University. We have developed algorithms and software for collection scheduling, remote sensor field-of-view pointing models, and bandwidthconstrained prioritization of sensor data. Our approach followed best practices from the operations research and computational geometry communities. These models provide several advantages over state of the art techniques. In particular, our approach is more flexible compared to heuristics that tightly couple models and solution techniques. First, our mixed-integer linear models afford a rigorous analysis so that sensor planners can quantitatively describe a schedule relative to the best possible. Optimal or near-optimal schedules can be produced with commercial solvers in operational run-times. These models can bemore » modified and extended to incorporate different scheduling and resource constraints and objective function definitions. Further, we have extended these models to proactively schedule sensors under weather and ad hoc collection uncertainty. This approach stands in contrast to existing deterministic schedulers which assume a single future weather or ad hoc collection scenario. The field-of-view pointing algorithm produces a mosaic with the fewest number of images required to fully cover a region of interest. The bandwidth-constrained algorithms find the highest priority information that can be transmitted. All of these are based on mixed-integer linear programs so that, in the future, collection scheduling, field-of-view, and bandwidth prioritization can be combined into a single problem. Experiments conducted using the developed models, commercial solvers, and benchmark datasets have demonstrated that proactively scheduling against uncertainty regularly and significantly outperforms deterministic schedulers.« less

Authors:
 [1];  [1];  [1];  [1];  [1];  [1];  [1];  [1];  [1];  [1];  [1];  [1];  [2];  [2]
  1. Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
  2. Texas A & M University, College Station, TX (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1562409
Report Number(s):
SAND-2016-9266
647529
DOE Contract Number:  
AC04-94AL85000
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English

Citation Formats

Valicka, Christopher G., Rowe, Stephen, Zou, Simon X., Mitchell, Scott A., Irelan, William R., Pollard, Eric L., Garcia, Deanna, Hackebeil, Gabriel Anton, Staid, Andrea, Rintoul, Mark Daniel, Watson, Jean-Paul, Hart, William E., Rathinam, Sivakumar, and Ntaimo, Lewis. Dynamic Multi-Sensor Multi-Mission Optimal Planning Tool. United States: N. p., 2016. Web. doi:10.2172/1562409.
Valicka, Christopher G., Rowe, Stephen, Zou, Simon X., Mitchell, Scott A., Irelan, William R., Pollard, Eric L., Garcia, Deanna, Hackebeil, Gabriel Anton, Staid, Andrea, Rintoul, Mark Daniel, Watson, Jean-Paul, Hart, William E., Rathinam, Sivakumar, & Ntaimo, Lewis. Dynamic Multi-Sensor Multi-Mission Optimal Planning Tool. United States. https://doi.org/10.2172/1562409
Valicka, Christopher G., Rowe, Stephen, Zou, Simon X., Mitchell, Scott A., Irelan, William R., Pollard, Eric L., Garcia, Deanna, Hackebeil, Gabriel Anton, Staid, Andrea, Rintoul, Mark Daniel, Watson, Jean-Paul, Hart, William E., Rathinam, Sivakumar, and Ntaimo, Lewis. 2016. "Dynamic Multi-Sensor Multi-Mission Optimal Planning Tool". United States. https://doi.org/10.2172/1562409. https://www.osti.gov/servlets/purl/1562409.
@article{osti_1562409,
title = {Dynamic Multi-Sensor Multi-Mission Optimal Planning Tool},
author = {Valicka, Christopher G. and Rowe, Stephen and Zou, Simon X. and Mitchell, Scott A. and Irelan, William R. and Pollard, Eric L. and Garcia, Deanna and Hackebeil, Gabriel Anton and Staid, Andrea and Rintoul, Mark Daniel and Watson, Jean-Paul and Hart, William E. and Rathinam, Sivakumar and Ntaimo, Lewis},
abstractNote = {Remote sensing systems have firmly established a role in providing immense value to commercial industry, scientific exploration, and national security. Continued maturation of sensing technology has reduced the cost of deploying highly-capable sensors while at the same time increased reliance on the information these sensors can provide. The demand for time on these sensors is unlikely to diminish. Coordination of next-generation sensor systems, larger constellations of satellites, unmanned aerial vehicles, ground telescopes, etc. is prohibitively complex for existing heuristicsbased scheduling techniques. The project was a two-year collaboration spanning multiple Sandia centers and included a partnership with Texas A&M University. We have developed algorithms and software for collection scheduling, remote sensor field-of-view pointing models, and bandwidthconstrained prioritization of sensor data. Our approach followed best practices from the operations research and computational geometry communities. These models provide several advantages over state of the art techniques. In particular, our approach is more flexible compared to heuristics that tightly couple models and solution techniques. First, our mixed-integer linear models afford a rigorous analysis so that sensor planners can quantitatively describe a schedule relative to the best possible. Optimal or near-optimal schedules can be produced with commercial solvers in operational run-times. These models can be modified and extended to incorporate different scheduling and resource constraints and objective function definitions. Further, we have extended these models to proactively schedule sensors under weather and ad hoc collection uncertainty. This approach stands in contrast to existing deterministic schedulers which assume a single future weather or ad hoc collection scenario. The field-of-view pointing algorithm produces a mosaic with the fewest number of images required to fully cover a region of interest. The bandwidth-constrained algorithms find the highest priority information that can be transmitted. All of these are based on mixed-integer linear programs so that, in the future, collection scheduling, field-of-view, and bandwidth prioritization can be combined into a single problem. Experiments conducted using the developed models, commercial solvers, and benchmark datasets have demonstrated that proactively scheduling against uncertainty regularly and significantly outperforms deterministic schedulers.},
doi = {10.2172/1562409},
url = {https://www.osti.gov/biblio/1562409}, journal = {},
number = ,
volume = ,
place = {United States},
year = {Thu Sep 01 00:00:00 EDT 2016},
month = {Thu Sep 01 00:00:00 EDT 2016}
}