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User preference optimization for oversubscribed scheduling of NASA's Deep Space Network
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SPRINT: Scheduling Planning Routing Intersatellite Network Tool
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Robust Cislunar Trajectory Optimization Via Midcourse Correction and Optical Navigation Scheduling
This paper presents a new approach to optimal trajectory design that considers uncertainties in the system, referred to herein as robust trajectory optimization. This approach assumes an existing reference trajectory and optimizes the locations of midcourse correction burns and utilization of onboard navigation sensors to minimize dispersions in ∆v or final position. Navigation errors, maneuver execution errors, orbit insertion errors, and environmental modeling errors are considered. The application in this paper is cislunar flight with the goal of injecting into a Near-Rectilinear Halo Orbit for rendezvous with a target vehicle. Two complementary optimization problems are proposed. One problem minimizes the total ∆v dispersion subject to a final position dispersion constraint. The other problem minimizes the final position dispersion subject to a total ∆v dispersion constraint. The results from each optimization problem are shown for a complete mission profile.
Scheduling Mission Reconfiguration for an Interferometry Synthetic Aperture Radar Using Deep Reinforcement Learning
This paper presents a method to intelligently adapt the baseline of a synthetic aperture radar based on Deep Rein- forcement Learning to help create plans for missions that use formation flight for Earth observation purposes. The main contribution of this paper is the initial results we have found from applying the tool to a toy mission: measuring the ver- tical structure of forests by using a synthetic aperture radar mounted on a formation of 7 satellites orbiting the Earth in a Sun Synchronous Orbit. We have found that with a reward function based on expected science return over time and fuel usage, the Deep Reinforcement Learning planner is able to create plans with positive scientific returns while minimizing fuel usage. We also find that fuel usage and collision avoid- ance planning is better done with traditional methods, as Deep Reinforcement Learning does not converge to optimal solutions.
Scheduling the mapping of a planet under geometrical constraints
No abstract provided
MSPA, Tracking Types, and Schedule Format Information for Artemis I SmallSats
No abstract provided
Towards Automated Scheduling of NASA’s Deep Space Network: A Mixed Integer Linear Programming Approach
No abstract provided
Scheduling the NASA Deep Space Network with Deep Reinforcement Learning
No abstract provided