Optimizing surface water monitoring by fusion of UAV and ASV platforms

Geological surveys often cover vast areas, traditionally requiring significant time and resources. Surveys of surface water are particularly challenging, as they typically rely on crewed vessels carrying specialized equipment, which is costly, potentially hazardous, and limited in both spatial and temporal resolution. With recent developments in drone technology, uncrewed platforms such as uncrewed aerial vehicles (UAVs) and autonomous surface vessels (ASVs) have increasingly been used in these applications, however, each has distinct limitations, and few attempts have been made to fuse both platforms for survey optimization. UAVs can rapidly cover large areas with high spatial resolution but provide low sensitivity as they are not in contact with the target, while ASVs deliver high sensitivity but are slow with limited coverage. This project proposes a multiplatform approach that combines UAVs, ASVs, and floating sensors to overcome these limitations with a focus on pinpointing specific targets, such as algal blooms. Algal bloom surveys can be conducted using a multistage approach where UAV-based hyperspectral scans identify regions of interest, which guide the targeted deployment of floating sensors via UAVs, whose data will inform where the ASVs should be deployed for high-sensitivity sampling. UAVs could also transport samples collected by ASVs to the shore or testing facilities, reducing transit times and increasing the number of samples taken. ASVs may additionally provide real-time validation of UAV surveys, offering a safer and more cost-effective alternative to traditional ground verification methods. In this project, we designed and 3D-printed a UAV deployable floating sensor frame, adopted a remote UAV drop-off and pick-up system, and updated a Seafloor Systems EchoBoat-160 ASV with improved internal hardware and software. This multiplatform strategy has the potential to transform water-based geological surveying by increasing efficiency, reducing operational costs, and enabling higher temporal and spatial resolution compared to both conventional and single-platform methods.