  The development of new AUV technology increased the range of tasks that AUVs
can tackle and the length of their operations. As a result, AUVs are capable of
handling highly complex operations. However, these missions do not fit easily
into the traditional method of defining a mission as a series of pre-planned
waypoints because it is not possible to know, in advance, everything that might
occur during the mission. This results in a gap between the operator's
expectations and actual operational performance. Consequently, this can create
a diminished level of trust between the operators and AUVs, resulting in
unnecessary mission interruptions. To bridge this gap between in-mission
robotic behaviours and operators' expectations, this work aims to provide a
framework to explain decisions and actions taken by an autonomous vehicle
during the mission, in an easy-to-understand manner. Additionally, the
objective is to have an autonomy-agnostic system that can be added as an
additional layer on top of any autonomy architecture. To make the approach
applicable across different autonomous systems equipped with different
autonomies, this work decouples the inner workings of the autonomy from the
decision points and the resulting executed actions applying Knowledge
Distillation. Finally, to present the explanations to the operators in a more
natural way, the output of the distilled decision tree is combined with natural
language explanations and reported to the operators as sentences. For this
reason, an additional step known as Concept2Text Generation is added at the end
of the explanation pipeline.
