Deep-space-distributed spacecraft missions continue to gain relevance as real-world missions such as the Laser Interferometer Space Antenna, Mars Sample Return, and others are conceived, designed, and flown. These classes of mission designs continue to challenge state-of-the-art trajectory optimization tools, often requiring significant changes to software and even new problem transcriptions entirely, in order to solve for optimal solutions. Trajectory optimization for multiple-vehicle missions poses all the challenges of the optimization of single-vehicle missions, but with added complexity of increased combinatorial scope due to multiple spacecraft, interspacecraft coordination constraints, and coordinated science objectives, motivating the need for new technical capabilities. The goal of this paper is to apply a multi-objective, multi-agent hybrid optimal control problem transcription, utilizing several new techniques, to optimize a very-long-baseline interferometry mission design. We develop and apply several new techniques in this capability, including a HashMap archive utility to prevent resolving missions, and a null gene transcription to vary both fleet size, and observation multiplicity. Applying these techniques enables efficient exploration the multi-objective nondominated front of a multivehicle design space where the number of spacecraft varies. The HashMap archive utility proves an essential component of this capability as duplicate candidate missions are discovered on average 17% of the time, and it yields an order of magnitude quicker lookup time compared with a text file analog. Within the resulting nondominated front of solution missions, many interesting solutions appear, including one mission with a fleet of 7 spacecraft that images 16 radio sources.


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    Title :

    Multi-Agent Multi-Objective Optimization of a Very-Long-Baseline Interferometry Mission


    Contributors:

    Published in:

    Publication date :

    2022-10-26


    Size :

    14 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English