# AUV Adaptive Sampling Methods: A Review
## 4 Mission Objectives of Sampling
### Front and Boundary Determination  
An ocean front is where two different types of water masses meet. The varying characteristics may include temperature, salinity and density. As an unusual vertical flow velocity at the frontal region may provoke biological activities and stimulate the ocean water circulation, it has become valuable to study front boundaries. [PERSON] and [PERSON] [89] presented a comparative framework of two methods for estimating the boundary of a neutral tracer injected into the ambient flow of a plume. The first method was the parametric approach using two models, an Extended Kalman Filter (EKF) and a non-linear regression. Variations in initial conditions were found to lead to instability in the tracking process and non-parametric approaches were suggested as a solution. The second method, a non-parametric approach, utilised learned empirical probabilistic distributions, which required little prior knowledge of the environment to track and delineate a plume. [PERSON] et al. [16] developed an adaptive approach to detect and track the front of a coastal upwelling region in Monterey Bay, CA, by identifying the frontal boundary based on the horizontal \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Sensor Reading** & **Manoeuvre** \\ \hline Rule 1 & Case 1: One sensor detects higher concentration & Turn to the side of the sensor \\  & Case 2: Both sensors detect the same concentration & Move forward \\ \hline Rule 2 & Case 3: Both sensors detect no salinity & Move backward \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Rules in the chemotaxis algorithm proposed by [PERSON] et al. [87]. gradient of the vertical temperature profile between the deep water and shallow water. Their approach consisted of two methods. The first method was to detect the upwelling water mass based on the vertical homogeneity of the temperature and the second method was to detect the upwelling front based on the horizontal gradient of the temperature [90]. They distinguished and classified three types of water mass as upwelling front, upwelling water and the normal stratified water. The [PERSON] long-range AUV was programmed to perform a sawtooth, also referred to as a yo-yo, trajectory in the vertical two-dimensional plane [6]. They developed an adaptive algorithm using the AUV to autonomously detect an upwelling and track the movement of the front on a fixed latitude [6; 16; 90; 91; 92]. The core of the method is to differentiate two types of water masses in an upwelling water column and a strongly stratified water column. Exploiting the knowledge on the vertical homogeneity of temperature, the algorithm classifies when the difference of temperature falls below a 1 \({}^{\circ}\)C (threshold) between a water depth of 5 m and 20 m. However, the numerical definition of the water depth (shallow and deep) and the ideal threshold can vary depending on the region of interest. They can be, respectively, determined from the preliminary initial survey. [PERSON] et al. [17] improved the coastal upwelling front tracking on a 'fixed latitude' method previously developed so as to be able to perform tracking and mapping in a 'four-dimensional field'. A four-dimensional survey, in this context, means the three-dimensional volumetric survey, as in a combination of horizontal zig-zag and vertical yo-yo tracking, in addition to time-variance. They also introduced an oblique angle of the zig-zag sweeping track, while conducting cross-front transection so that the AUV would zig-zag through the front at a near-normal angle. This might require a collaborative effort to provide the AUV with the information of the normal spreading direction in the given area. However, in terms of the thresholds of the vertical temperature, it is more desirable for the AUV to learn about the water column in the survey region and adaptively adjust the thresholds so as to capture the movement of the front with a high temporal and spatial resolution.