NEURAL NETWORKS FOR PLUME DETECTION: INTERPLANETARY CUBESAT CASE STUDY

In the last few years, CubeSat missions have been pushed towards a new direction: interplanetary exploration. Several challenges undermine the feasibility and the good success of these missions: two of the most critical ones are limited data rates available on CubeSats, and the necessity of performing autonomous operations. This paper deals with the latter problem: autonomous on board operations for a CubeSat mission to an asteroid. In particular, the work presented focuses on external event detection, performed by processing images acquired by a camera sensor. The objective of this work is to demonstrate the capability of artificial neural networks to successfully detect the emission of plumes from a comet-like body, and to identify the orientation of such emissions, with the intent of providing information to the guidance, navigation and control system of the spacecraft, be it for avoidance manoeuvre planning or for enhanced science operations. Results of the paper demonstrate that employing neural networks for event detection is feasible and provides interesting outcomes. In addition, the proposed algorithm can be further coupled with GNC algorithms towards the development of an autonomous interplanetary small satellite.