File:Enhancing the science collection capability of NASA's Lunar Reconnaissance Orbiter (LRO) (IA enhancingscience1094556755).pdf

The mission of NASA's Lunar Reconnaissance Orbiter (LRO) was to map the Moon's surface when it was launched in 2009. Since then, LRO's mission set has expanded to include providing scientific data to contribute to a better understanding of the lunar surface, its history, potential lunar habitat, as well as a more general understanding of human spaceflight for future lunar or other terrestrial missions. With seven different payload instruments onboard the LRO, daily operations of the spacecraft are requested by different scientist communities and ultimately approved and implemented through the LRO mission operations center. The mission effectiveness is limited by the target planning process and the vehicle capabilities. Currently, NASA Goddard is interested in improving the throughput of the mission. The focus of this thesis is to address this challenge and to develop an automated process for target selection as well as solve for a rapid slew to the desired targets. An automated target selection strategy is developed based on bipartite graph theory. An example is presented that demon-strates the usefulness of this approach. To ensure the plan can be executed and the science objectives satisfied, rapid slew maneuvers are developed using optimal control theory. A key challenge to the rapid slew is meeting operational constraints, which are treated as path constraints in optimal control. It is shown that the slew time for a payload instrument science target can be reduced by up to 50%.

Subjects: optimal control; target selection; minimum-time maneuvers; rapid slew; reaction wheel; bipartite graph theory; Lunar Reconnaissance Orbiter