# AUV Adaptive Sampling Methods: A Review
## 5 Multiple Platforms Networking
### Multiple Agents Networking
They demonstrated a preliminary simulation for a cooperative temperature-front tracking using the proposed adaptive sampling and Innovative Coastal-Ocean Observation Network (ICON) as the ocean forecast model. ICON provided two data sets: a 'forecast set' and a 'true set'; the latter is the satellite sea surface temperature data (SST) observed by the aircraft. The forecasting model generates the prediction and then programs the gliders with a daily sampling plan. The vehicles' paths and formations are modified through observation and measurements using the coordinated and cooperative control. Their comparison is summarised in Table 14. The key aim of the adaptive sampling in their method was to keep the gliders in formation so as to obtain gradient estimates through a least-squares approach. Various forms of configuration have been suggested for a fleet of gliders in formation, as shown in Table 15. Three is the minimum number of vehicles to estimate gradients. The ring-type configuration is an optimal formation, providing good gradient estimates. The Virtual Body and Artificial Potential (VBAP) method has been presented to control a glider fleet [18]. The concept of VBAP was originally developed for a general vehicle [102]. The purpose of VBAP is to coordinate a group of point masses. The artificial potentials provide a simple expedient of fleet formation or reconfiguration by defining the interactions between vehicles and reference points on a virtual body (virtual leaders). A variety of formations have been presented, applying the VBAP method for coordinated adaptive sampling. The triangle formation is suited for estimating gradients on each side. The line formations \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Input** & **Responsibility** & **Purpose** \\ \hline Coordinated & GPS fixes of the gliders & Enable the paths, & \\ control & Local current estimates & formations and patterns & Used in post-processing \\  & GPS fixes of the gliders & & \\ Cooperative & Local current estimates & & \\ control & Gliders’ sensor & & \\ measurements & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Coordinated control and cooperative control implemented in an adaptive sampling approach with multiple vehicles. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Number of**} & \multirow{2}{*}{**3**} & \multirow{2}{*}{**4**} & \multirow{2}{*}{**5**} \\ \multicolumn{2}{c}{**Vehicles**} & & & & \\ \hline Formation & & & & & \\ \hline \multirow{4}{*}{Detail} & Ring-type: Optimal for & Line-type: Useful & & \\  & Optimal for & for computing & Square: & triangle: Useful & Pentagon: & formation: \\  & estimating & directional & Optimal for & for second-order & Optimal for & Useful for \\  & gradients in the & derivatives along & estimating & derivative & estimating & second-order \\  & presence & the direction of & gradients & computation & gradients & derivative \\  & of noise & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 15: A variety of configurations for a fleet of gliders in accordance with its number involved [18]. provide a good coverage, alternating both sides of the group's mean path. Directional derivatives perpendicular to the direction of travel and those in the direction of travel can be obtained. Finally, the hybrid formation enables the fleet to concurrently survey on both sides of the mean path, while full-gradient estimates as well as the directional derivatives are to be collected. A least-square method was utilised for gradient estimation at the centre of the virtual body [103]. Their coordinated control approach was demonstrated in the field trial to respond to the onset of an upwelling event in Monterey Bay [19]. Three different schemes of Kalman filters (KF) in an adaptive sampling algorithm for multi-robots are extensively described and their respective results are presented by [PERSON] et al. [95]. The characteristics of the filters in the use of adaptive sampling are compared in Table 16.  
[PERSON] et al.