# AUV Adaptive Sampling Methods: A Review
## 3 Target of Interest
### Chemical Features
#### Oil Plume
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Direct Hydrocarbon Detection System**} \\ \hline Target feature & Sensor & Method \\ \hline Methane (CH4) & NDIR spectrometry & Measuring methane (CH4) \\ \hline Polycyclic aromatic & & Measuring the intensity and \\ hydrocarbons (PAHs) & Fluorometer & wavelength distribution of the \\  & & emission spectrum \\ \hline Chromophoric dissolved & & Measuring the concentration of \\ organic matter (CDOM) & CDOM fluorometer & refined hydrocarbons (300 nm) or \\  & & crude hydrocarbons (440 nm) \\ \hline Particle sizes & LISST instrument & Measuring particle size \\  & & distribution \\ \hline Dispersed hydrocarbons in the & Subsea camera/video technology & Characterising dispersed \\ water column & (SINTEF silhouette camera) & hydrocarbons in the water column \\ \hline Ion compositions & Mass spectrometer & Capillary gas chromatography (CGC)–mass spectrometry (MS) \\  & & method \\ \hline \multicolumn{3}{c}{**Indirect Hydrocarbon Detection System**} \\ \hline Target feature & Sensor & Method \\ \hline Conductivity, temperature and & CTD sensor & Deriving salinity from hydrostatic \\ depth & & pressure measurement \\ \hline Turbidity & Optical light scatter & Measuring turbidity \\ \hline Dissolved oxygen & Electrodes, electrochemical sensors & Measuring oxygen concentration \\ concentration & or optodes (optical sensors) & \\ \hline Dissolved CO2 concentration & NDIR spectrometry & Measuring CO2 concentration \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Available in-situ methods for hydrocarbon detection in oil spill surveillances, developed from information in [63]. were integrated to aid in selecting the survey site and search design. The classic Sense-Plan-Adapt (SPA) strategy was applied incorporating human input (as a higher-level-decision-making agent) at every iteration. Utilising Gaussian Process Classification (GPC) regression [69], chemical data was interpreted and converted into three classifications: plume, background and others (e.g., dissolved oxygen concentration). Human analysis was essential for data interpretation at the Sense stage. Moreover, they recommended to incorporate more human insight to decide when to make an early termination of track lines. Their method pursues a well-balanced strategy to exploit humans' ability to reason. It can only be used where consistent supervision and real-time communication is available. [PERSON] et al. [67] modified the adaptive triggering algorithm that originally targeted the thin phytoplankton layer. Unlike the underlying assumption of the biological features, the peak magnitude increases or decreases depending on the direction the vehicle is heading relative to the source. Therefore, their algorithm was modified in accordance with the attributes of the oil. Instead of peak magnitudes, the depths of the peak were assumed to be similar between two consecutive crossings. Another aspect of their strategy is that the background signal-level and baseline of the peaks were continuously observed in real time to ensure that the true high peaks were captured. Keeping track of the background level ensures that the algorithm is tuned with the ambient conditions and only the true high peaks are triggered. However, the adaptive approach was applied in a limited sense in this case. The vertical yo-yo range was so large between the depths at 900-1200 m that few portions of the oil plume were detected. To sum up, any gradient-based methods have a potential risk to become stuck in local maxima, especially in the absence of a well-defined gradient. On the other hand, a bio-mimetic method generally tracks the crossflow direction to find traces of the plume and hence, localise the source. In addition, purely reactive-based algorithms tend to be less robust when dealing with multiple-source problems such as oil seeps.