Fast Approximators For Optimal Low-Thrust Hops Between Main Belt Asteroids

2016 IEEE Symposium Series on Computational Intelligence (SSCI)(2016)

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摘要
We consider the problem of optimally transferring a spacecraft from a starting to a target asteroid. We introduce novel approximations for important quantities characterizing the optimal transfer in case of short transfer times (asteroid hops). We propose and study in detail approximations for the phasing value phi, for the maximum initial mass m* and for the arrival mass m(f). The new approximations require orders of magnitude less computational effort with respect to state-of-the-art algorithms able to compute their ground-truth value. The accuracy of the introduced approximations is also found to be orders of magnitude superior with respect to other, commonly used, approximations based, for example, on Lambert models. Our results are obtained modelling the physics of the problem as well as employing computational intelligence techniques including the multi-objective evolutionary algorithm by decomposition framework, the hypervolume indicator and state of the art machine learning regressors.
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关键词
machine learning regressors,hypervolume indicator,decomposition framework,multiobjective evolutionary algorithm,computational intelligence techniques,ground-truth value,arrival mass approximation,maximum-initial mass approximation,phasing value approximation,transfer times,optimal spacecraft transfer,main-belt asteroids,optimal low-thrust hop approximator
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