  The recent advance in autonomous underwater robotics facilitates autonomous
inspection tasks of offshore infrastructure. However, current inspection
missions rely on predefined plans created offline, hampering the flexibility
and autonomy of the inspection vehicle and the mission's success in case of
unexpected events. In this work, we address these challenges by proposing a
framework encompassing the modeling and verification of mission plans through
Behavior Trees (BTs). This framework leverages the modularity of BTs to model
onboard reactive behaviors, thus enabling autonomous plan executions, and uses
BehaVerify to verify the mission's safety. Moreover, as a use case of this
framework, we present a novel AI-enabled algorithm that aims for efficient,
autonomous pipeline camera data collection. In a simulated environment, we
demonstrate the framework's application to our proposed pipeline inspection
algorithm. Our framework marks a significant step forward in the field of
autonomous underwater robotics, promising to enhance the safety and success of
underwater missions in practical, real-world applications.
https://github.com/remaro-network/pipe_inspection_mission
