Monitoring water bodies is not only important for understanding the physiology of aquatic life but also for understanding how these systems are affected by both natural changes in the environment such as storms and volcanic eruptions as well as human activities such as surface run off's from farms and industrial discharges. By collecting spatially distributed samples and analyzing the data it may be possible to predict how some of these processes work and potentially prevent adverse ecological effects such as eutrophication, oxygen depletion, and accelerated aging.
Our autonomous airboats have the capability to cooperatively sample large areas while providing real-time measurements with very little human supervision. Some of our previous deployments include ponds, canals, rivers, lakes and fish farms.
Early field testing was done at an irrigation pond at a nursery. This pond is scientifically interesting because the nursery recycles the water, spraying the plants with water from the pond then capturing the run-off back in the pond. This approach is environmentally exciting, as it reduces water waste, but there is a concern regarding water quality over time due to accumulating fertilizers and pesticides. Biologists have two stationary buoys in the pond, measuring various properties of the water. We deployed three boats out at the lake over four days of testing. A key aim was to sense across the whole pond, to interpolate between the data collected by the biologists. We used sensors that measured electrical conductivity, a property of water that correlates well with the total dissolved solids in the water, a key measure of interest to scientists as well as temperature and pH.
The above figures show a plot of the electrical conductivity and the temperature across the pond, as measured by the boats. Notice that both measures vary significantly across the pond, with the scientist’s fixed buoys (which were placed near the top right and bottom left of the pond) giving only part of the picture. This shows the value of using mobile sensors like watercraft to sample the pond. During this test, we tried simple sampling patterns (primarily a lawnmower pattern) and a simple adaptive sampling algorithm. The adaptive sampling al- gorithm would send the boat to the location where previous readings had shown maximum uncertainty, intuitively attempting to minimize overall uncertainty as quickly as possible. However, it turned out that uncertainty was relatively uniform across the pond and the adaptive sampling worked qualitatively the same as the simple patterns.