Adaptive Zigzag Mapping of a Patchy Field by a Long-Range Autonomous Underwater Vehicle

Spatial heterogeneity and temporal evolution are intrinsic features of some important oceanic processes, e.g., harmful algal blooms and oil spills, where aggregations of organisms or materials are localized and noncontinuous. In a sparse patchy field, routine lawnmower-mode or zigzag surveys by ships or autonomous platforms are not efficient since a large proportion of the survey time is spent on no-patch areas. We developed an adaptive zigzag algorithm for an autonomous underwater vehicle (AUV) to map patchy fields more efficiently than routine zigzag surveys. An AUV autonomously detects the peak and the edge of a patch, and accordingly determines when to turn onto the next zigzag leg. The AUV sweeps through the field on successive zigzag legs. Using an oil spill model data set, the performance of adaptive zigzag surveys is compared with that of routine zigzag surveys. In April 2022, the algorithm was tested on a long-range AUV through a 16-h survey in Monterey Bay, CA, USA, by reading the oil spill model data as the virtual measurement in real time.