Toward adaptive robotic sampling of phytoplankton in the coastal ocean
Toward adaptive robotic sampling of phytoplankton in the coastal ocean
复制标题
DOI：
10.1126/scirobotics.aav3041
复制
发表时间：
2019-02-13
期刊：
影响因子：
25
通讯作者：
Rajan, Kanna
中科院分区：
文献类型：
--
作者：
Fossum, Trygve O.;Fragoso, Glaucia M.;Rajan, Kanna
洋流、风、水深测量和淡水径流是导致沿海水域异质性、斑块性和科学趣味性的一些因素，在这些因素中，解决水柱内的时空变化是具有挑战性的。我们介绍了使用具有嵌入式算法的自主水下航行器（AUV）进行现场实验的方法和结果，该算法将采样集中在三维特征上。这是通过将高斯过程（GP）建模与机载机器人自主性相结合来实现的，允许在精细尺度上进行体积测量。特别关注浮游植物生物量的斑块性，叶绿素a （Chla）是理解沿海海洋生物地球化学过程（如初级生产力）的重要因素。在挪威Runde的多次现场测试中，该方法成功地用于识别、绘制和跟踪地下叶绿素a最大值（SCM）。结果表明，该算法能够从体积上估计SCM，使AUV能够跟踪体积内的最大浓度深度。随后，对这些数据进行了核实和补充，包括遥感、浮标的时间序列和快速重复率荧光计的船上测量、粒子成像系统以及离散的水样，涵盖了由沿海动态形成的微生物群落的大小尺度。通过汇集统计学、自主控制、成像和海洋学等多种方法，这项工作为机器人观察不断变化的海洋提供了跨学科的视角。
Currents, wind, bathymetry, and freshwater runoff are some of the factors that make coastal waters heterogeneous, patchy, and scientifically interesting-where it is challenging to resolve the spatiotemporal variation within the water column. We present methods and results from field experiments using an autonomous underwater vehicle (AUV) with embedded algorithms that focus sampling on features in three dimensions. This was achieved by combining Gaussian process (GP) modeling with onboard robotic autonomy, allowing volumetric measurements to be made at fine scales. Special focus was given to the patchiness of phytoplankton biomass, measured as chlorophyll a (Chla), an important factor for understanding biogeochemical processes, such as primary productivity, in the coastal ocean. During multiple field tests in Runde, Norway, the method was successfully used to identify, map, and track the subsurface chlorophyll a maxima (SCM). Results show that the algorithm was able to estimate the SCM volumetrically, enabling the AUV to track the maximum concentration depth within the volume. These data were subsequently verified and supplemented with remote sensing, time series from a buoy and ship-based measurements from a fast repetition rate fluorometer (FRRf), particle imaging systems, as well as discrete water samples, covering both the large and small scales of the microbial community shaped by coastal dynamics. By bringing together diverse methods from statistics, autonomous control, imaging, and oceanography, the work offers an interdisciplinary perspective in robotic observation of our changing oceans.