# AUV Adaptive Sampling Methods: A Review
## 2 Process of Adaptive Sampling  
An AUV is normally fitted with sensors in order to undertake a mission. After sensor measurements are collected, they are analysed and interpreted into meaningful data. In an adaptive mission and based on the analysed data, an existing mission plan may be modified or renewed to optimise the AUV path for subsequent measurements. One example of an adaptive sampling procedure may include: (1) investigate an initial set of sparse observations, which leads to the question of where to take the next measurements, and (2) revisit and conduct a finer scale survey. The underlying autonomous structure of the procedure requires three basic segments: sensing, diagnosis and adaptation. They resemble the cognition process of many biophysical entities. These steps are recursively executed in a closed loop process as shown in Figure 1. In the perceptive phase, the collected measurements are interpreted into meaningful data, while conforming to the current mission plan. Then, in the behavioural phase, details of the mission plan are updated to suit the mission goal/s. A description of each step is given below. Sensing: An AUV can acquire knowledge about its surroundings through taking measurements using a variety of sensors. Sensing the environment or perceiving the phenomena \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Targeted Feature of Interest**} \\ \hline Physical & Biological & Chemical \\ \hline Thermocline & Phytoplankton layer & Hydrothermal vent \\ Upwelling & Zooplankton layer & Produced water/waste water \\ Internal wave & Harmful algal bloom & Natural oil seep/Oil spill \\ \hline \multicolumn{3}{c}{**Objective of Sampling Mission**} \\ \hline Source localisation & Front/Boundary determination & Tracking and Mapping \\ \hline Bio-mimetic & Gradient anomaly & Adaptive transect mapping \\ Gradient search & Isothermal line & Zig-zag feature-based mapping \\ NMPC scheme & Control-based technique & \\ \hline \multicolumn{3}{c}{**Multi-Vehicle Networking**} \\ \hline A pair of vehicles & \multicolumn{3}{c}{Multiple vehicles} \\ \hline Leader–follower & Leader-followers & Formation-based approach \\ Track and trail & Virtual body and artificial & Coordinated/Cooperative \\  & potential (VBAP) & sampling \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Categories of adaptive sampling. Figure 1: A closed-loop process of adaptive sampling consisting of three segments: perception, diagnosis and adaptation. is the most crucial part in the course of operation of the AUV. It allows the AUV to comprehend the survey area, and therefore, to plan a better path. Especially in an unstructured environment, the AUV must rely on its sensor measurements to gather information. This _sensing_ step has significance in two regards: being closely related to the vehicle survey path and appropriate sensor selection. The path or trajectory of unmanned vehicles influences the result of sensing performance. For example, algal blooms in the ocean appear in the horizontal plane at various depths over time. Therefore, a vertical saw-tooth AUV trajectory is commonly adopted to delineate their vertical migration. In short, the trajectory of the vehicle is determined depending on the distribution of the target in water. Some measurements are simple parameters that are readily extracted and easily interpreted, such as the temperature of the ambient water or the speed of the AUV. The depth of the AUV is measured by using a pressure sensor. More complicated sensors, such as fluorometers or an underwater mass spectrometer, take biological or chemical measurements that require further interpretation to convert them into meaningful data. Selection of appropriate sensors is the key in successful sensing or detection. Table 2 summarises a list of example sensors for several different features. Diagnosis: After the measurements are taken in the previous step, they undergo diagnosis.