  The design of low-thrust-based multitarget interplanetary missions requires a
method to quickly and accurately evaluate the low-thrust transfer between any
two visiting targets. Complete evaluation of the low-thrust transfer includes
not only the estimation of the optimal fuel consumption but also the judgment
of transfer feasibility. In this paper, a deep neural network (DNN)-based
method is proposed for quickly evaluating low-thrust transfer. An efficient
database generation method is developed for obtaining both the infeasible and
optimal transfers. A classification DNN and a regression DNN are trained based
on the infeasible and optimal transfers to judge the transfer feasibility and
estimate the optimal fuel consumption, respectively. The simulation results
show that the well-trained DNNs are capable of quickly determining the transfer
feasibility with a correct rate of greater than 98% and approximating the
optimal transfer fuel consumption with a relative estimation error of less than
0.4%. The tests on two asteroid chains further show the superiority of the
DNN-based method for application to the design of low-thrust-based multitarget
interplanetary missions
