Adaptive AUV Planning For Robust Optical Object Inspection Using In-Situ Sonar Data

Optical imaging for identifying targets of interest is an important operational phase in many applications of autonomous underwater vehicles (AUVs). However, underwater optical imaging is challenging due to limited visibility and rugged terrain. To counter the low visibility, the AUV needs to fly at a low altitude over the targets. This results in a small optical footprint and corresponding small margins for error in maneuvering and navigation accuracy, in addition to a significant risk of collision with the seabed. In this paper, we propose a planning algorithm that adapts the mission to in-situ knowledge for safe and robust optical inspection of seafloor objects. By using automatic target recognition on data from a multibeam echosounder with a wider field of view, the algorithm checks whether the target was within the camera footprint. For targets that were deemed outside, i.e., missed, the plan is updated with the corrected object positions. This significantly increases the probability of successful optical inspection in a reduced amount of time, as well as reduces the risk of collision. The proposed method has been demonstrated in sea experiments using a HUGIN AUV.