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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Multi-objective Reinforcement Learning for Low-thrust Transfer Design Between Libration Point Orbits

Multi-Reward Proximal Policy Optimization (MRPPO) is a multi-objective rein- forcement learning algorithm used to construct low-thrust transfers between pe- riodic orbits in multi-body systems. Previous implementations of MRPPO have relied on a predefined reference transfer to successfully train each policy. In this paper, an algorithmic modification labeled the ‘moving reference’, is introduced to autonomously construct these reference trajectories during training. With this modification, MRPPO is used to recover various low-thrust transfers between two periodic orbits in the Earth-Moon circular restricted three-body problem to solve a multi-objective optimization problem. These results are then compared with the solutions recovered via a gradient descent optimization scheme to validate the performance of MRPPO with the moving reference modification.

Anderson, Rodney L↗
NASA NTRSDate not supplied

Reinforcement Learning for Spacecraft Navigation & Environment Characterization in the Planar-Restricted Two-Body Problem

During mission planning and execution, spacecraft operators must balance data collection and downlink, systems constraints, human factors, and navigation. As missions become increasingly complex and ambitious, these factors become more intricately entwined and conflicted. For example, a spacecraft’s position must be known accurately in order to point to and image a target. Large position errors may cause missed observations or require additional scanning that increases operations complexity and data volume. Some observations require imaging from specific relative geometries which adds orbit control and timing considerations. Adjusting the orbit may allow for optimal observability of environmental parameters and/or enable more efficient sensor coverage, but maneuver execution error adds uncertainty to the current state which impacts both characterization and coverage objectives.

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