optimal path planning of a spacecraft via a deep neural network for soft landing on the irregular-shaped 433 eros asteroid

This paper aimed to utilize a deep neural network (dnn) to achieve optimal path planning for a spacecraft during a landing mission on an asteroid. a minimum energy-consumption mission is evaluated in which a dnn is utilized to predict the optimal path in case of any failures or unforeseen alterations. the paper uses a dnn and employs a polyhedral model, which is renowned as the most precise method for modelling the irregular shapes of asteroids. the dnn, is utilized for path planning and incorporates data calculated by the network into a spacecraft dynamics equations where an intelligent supporter model has been developed to handle the high computation load of the gravitational field of polyhedral models. moreover, this study indicates that the prediction errors of final locations are less than 1 kilometer, as the training errors of networks are deemed entirely satisfactory. eventually, the feasibility of the proposed approach is demonstrated through corresponding simulations