Optimal path planning of spacecraft fleet to asteroid detumbling utilizing deep neural networks and genetic algorithm.

• An optimal path planning is proposed for spacecraft fleet to asteroids detumbling. • Optimal low-thrust trajectories for the spacecraft fleets in a 3D high perturbed asteroid environment is designed through a combined GA-Collocation method. • A deep neural network is used to rapidly approximate the gravitational acceleration of the asteroid. • The optimal problem utilized optimization method, as GA for guessing systematically the optimal initial costates, with high speed and accuracy be able to it yield consistent and reliable results. Following space discoveries, asteroids were found as rich sources of minerals and organic matter that can be exploited. In this paper, we present a precise, fast, and robust energy-optimal soft landing with a combined GA-Collocation method for a spacecraft fleet aimed at an irregular asteroid detumbling mission. It is assumed that the spacecraft fleet carried to the asteroid's equilibrium points introduced as the start locations of the mission. Here, the 433 Eros asteroid has been considered as the stony target known for its irregular elongated shape and nearly large dimension. We modeled the gravitational potential field by polyhedron as the most accurate one for such asymmetric objects. Hence, to decline the high burden computational of related equations in gravitational acceleration calculation, a Deep Neural Network with seven hidden layers and a relatively large dataset covering 6000 pairs has been developed. We utilized a Genetic Algorithm to guess systematically more optimal and reliable initial costates of some key parameters to raise the speed and accuracy of estimation. The acceptable efficiency and accuracy in optimal path prediction of the presented approach for a fleet of spacecraft approved throughout precise simulations. Eventually, the feasibility of the proposed approach is demonstrated through the corresponding results. [ABSTRACT FROM AUTHOR]