Towards safely automating space mission operations

The space industry has always been a leading sector in technology development and research, pushing for automating operations in harsh environmental conditions and with high safety-critical scenarios. Automating space mission operations can make their management more efficient. The space case study in SAFEXPLAIN adopts the use of Deep Learning models for visual processing in a rendezvous and docking scenario. The algorithm exploits a vision-based DL algorithm to compute the position estimation of the target. The task can be particularly challenging as images are captured in grayscale and generally quite low resolution; they are also generated in different conditions of illumination and poses, different backgrounds (space, the Earth, other objects in orbit) and the noise coming from the real optical sensors.