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Multiple autonomous underwater vehicles (AUVs) working in collaboration can achieve scientific goals more effectively than independently operated vehicles. In this article, we present a case of using two long-range AUVs (LRAUVs) for persistent environmental DNA (eDNA) sampling of a targeted feature. Each LRAUV was equipped with a third-generation environmental sample processor (3G-ESP), a robotic instrument for acquiring and processing water samples for molecular analysis. Each 3G-ESP can collect and process 60 samples. For continuous and persistent eDNA sampling of vertically migrating organisms at a targeted depth layer, we deployed two LRAUVs which alternately triggered the ESP, extending the total time of collecting samples. We developed a method of coordinated sampling by time shift and a collaborative sampling method that uses acoustic handshakes. In the time-shift method, each vehicle switched between two behaviors: sample collection at the targeted depth and spiraling over a large depth range to make contextual measurement. The second vehicle's mission started later than the first vehicle's by a time shift equal to the duration of one sampling event, such that at a given time one vehicle sampled at the targeted depth while the other vehicle spiraled up and down. In the acoustic-handshake method, the two LRAUVs exchanged sample-start and sample-end messages. On receiving vehicle #1’s sample-end message, vehicle #2 triggered a sampling event and transmitted a sample-start message to vehicle #1. Then, vehicle #1 waited for vehicle #2’s sample-end message before triggering the next sampling event, and so forth. The time-shift method is simple, whereas the acoustic-handshake method is accurate and adaptive. Both methods were demonstrated in experiments in Monterey Bay.

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.