Collaborative Research: A Multi-Modal Optical Sensing Package for Autonomous Adaptive Biogeochemical Sampling and Particle Characterization

Suspended particulate material is a complex mixture of living and detrital organic and inorganic material. The composition and concentration of suspended particulate material vary greatly throughout the ocean and can be considerably influenced by local processes. The microbial and biogeochemical processes occurring within this material greatly influence the flow of carbon and other nutrients through the marine environment.

The scale of the ocean makes these particulate processes globally relevant and, at the same time, challenging to fully characterize. Equipping robotic vehicles with a sensor system capable of rapidly distinguishing environmental variations in marine particle size, shape, and composition will enable navigation based on the properties of the ambient particle field.

More specifically, it will enable the targeted collection of samples by autonomous robotic vehicles to those zones of the ocean where particle processes are most relevant and dynamic. This research will develop an optical sensing instrumentation suite and integrate it with existing robotic sampling tools for both remotely operated vehicle Jason and the autonomous underwater vehicle Clio and optimize their use to characterize hydrothermal plume particle processes in an engineering sea trial in the vent fields of the Juan de Fuca Ridge.

This research will develop an optical sensing system to directly characterize marine particles and enable adaptive robotic collection of biogeochemical and biological samples from autonomous vehicles and remotely operated platforms. The optical sensing components of this system will characterize marine particles based on multiple parameters indicative of size, shape, and composition.

Optical sensing will be based on a tightly integrated sensor suite consisting of camera-based particle imaging, using both wide-field stereoscopic and microscopic cameras, fluorometry, and optical transmission sensors. These sensors will be controlled by a single board computer capable of running real-time classification algorithms that can be used to control adaptive particle and fluid sampling systems.

The close integration of the sensing elements is intended to both achieve a smaller overall payload size and allow for maximum control of sensing parameters including timing, sequencing, and frequency. The intent is to maximize the use of open-source software and hardware so that the resulting design can be shared within the broader community to allow for modification, adaptation, and experimentation.

The goal is to improve the oceanographic community's ability to target novel biogeochemical environments using robotic oceanographic vehicles so that we can more efficiently study geochemically important environments in an otherwise very large ocean. This optical sensing system will be designed to enhance the observational capabilities of both large and small underwater vehicles and platforms.

The system will consist of a stereo camera pair, a flow-cell/microscope camera, a commercial chlorophyll-a fluorometer, a commercial backscatter sensor, an electronic stack in a custom pressure housing, and an adaptive sampling subsystem. The operation of the system will be tested both in the laboratory and in field on remotely operated vehicle Jason and autonomous underwater vehicle Clio in the hydrothermal plumes of the Juan de Fuca Ridge during an engineering sea trial cruise in 2026.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.