Autonomous spacecraft control in the solar gravitational lens' focus via reinforcement learning

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Abstract

The problem of autonomous control of the translational motion of the spacecraft in the vicinity of the focus of the gravitational lens of the Sun is formulated. The problem is solved by a reinforcement machine learning method using contemporary stochastic numerical methods. The costs of the characteristic velocity for targeting the focal line of a remote extended source, the final accuracy of targeting and the quality of the control function are investigated. The results of the study are given for various forms of state and observation: 1) position and velocity, 2) noisy position and velocity, 3) image of the Einstein ring. The efficiency of control strategies when using recurrent layers and fully connected layers with an input in the form of a measurement stack is compared. The training of control models accounting for execution errors of maneuvers is also being explored.

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About the authors

M. G. Shirobokov

Keldysh Institute of Applied Mathematics

Author for correspondence.
Email: shirobokov@keldysh.ru
Russian Federation, Miusskaya Pl., 4, Moscow

K. R. Korneev

Keldysh Institute of Applied Mathematics

Email: shirobokov@keldysh.ru
Russian Federation, Miusskaya Pl., 4, Moscow

D. G. Perepukhov

Keldysh Institute of Applied Mathematics

Email: shirobokov@keldysh.ru
Russian Federation, Miusskaya Pl., 4, Moscow

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Supplementary files

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2. Fig. 1. Schematic representation of the curvature of light rays from a distant source under the influence of a solar gravitational lens.

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3. Fig. 2. Image formed by glafic2 for the spacecraft at a distance of 130 thousand km from the focal line and at a distance of 600 AU from the Sun.

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4. Fig. 3. Image formed by glafic2 for the spacecraft at a distance of 85 thousand km from the focal line and at a distance of 600 AU from the Sun.

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5. Fig. 4. Initial and final distances (blue dots) to the focal line obtained in a series of Monte Carlo trials for the strategy based on the state of the vehicle; the line of equality of distances is marked in red.

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6. Fig. 5. Initial and final distances (blue dots) to the focal line obtained in a series of Monte Carlo tests for the strategy based on the state of the vehicle.

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7. Fig. 6. Initial and final distances (blue dots) to the focal line obtained in a series of Monte Carlo trials for the image stack-based strategy.

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