Implementation of Artificial Neural Networks for the Nepta-Uranian Interplanetary (NUIP) Mission

Abstract

A celestial alignment between Neptune, Uranus, and Jupiter will occur in the early 2030s, allowing a slingshot around Jupiter to gain enough momentum to achieve planetary flyover capability around the two ice giants. The launch of the uranian probe for the departure windows of the NUIP mission is between January 2030 and January 2035, and the duration of the mission is between six and ten years, and the launch of the Nepta probe for the departure windows of the NUIP mission is between February 2031 and April 2032 and the duration of the mission is between seven and ten years. To get the most out of alignment, deep learning methods are expected to play a critical role in autonomous and intelligent spatial guidance problems. This would reduce travel time, hence mission time, and allow the spacecraft to perform well for the life of its sophisticated instruments and power systems up to fifteen years. This article proposes a design of deep neural networks, namely convolutional neural networks and recurrent neural networks, capable of predicting optimal control actions and image classification during the mission. Nepta-Uranian interplanetary mission, using only raw images taken by optimal onboard cameras. It also describes the unique requirements and constraints of the NUIP mission, which led to the design of the communications system for the Nepta-Uranian spacecraft. The proposed mission is expected to collect telemetry data on Uranus and Neptune while performing the flyovers and transmit the obtained data to Earth for further analysis. The advanced range of spectrometers and particle detectors available would allow better quantification of the ice giant's properties.