# AUV Adaptive Sampling Methods: A Review
## 5 Multiple Platforms Networking
### Multiple Agents Networking  
Underwater missions involving multiple vehicles enable sharing of in-situ data in real-time and provide multiple data points simultaneously. However, coordinated control is more challenging. The bandwidth and communication constraints underwater are the endemic issues of AUV operations. The risks increase more in the context of multi-AUV operations. An adaptive sampling solution to the problem is often sought from the model-based, observation-driven data assimilation. This section describes various methods utilising multi-vehicles. When multi-vehicles are deployed, they often and should work as a team in a coordinated fashion. Their roles may be given equal weight or arranged in a hierarchical structure. A proposed real-time unsupervised adaptive sampling strategy using a fleet of AUVs was introduced by [PERSON] et al. [100]. This strategy was derived from the difficulty in utilising a single AUV with limitations in operating duration, power, and uncertainty in obtained data. They developed a Gaussian Mixture Model (GMM) to model the CTD data acquired in real-time. The adaptive sampling strategy in their method required at least two vehicles. The aim would be that one vehicle is designated as a virtual leader to track the desired pre-defined path while providing other vehicles with a moving reference. Each follower vehicle would obtain the GMM hypothesis of CTD data from the leader vehicle. The variational distance error between the received data and its own GMM was computed. This measured dissimilarity (error or variational distance) of the CTD profiles was used to reconfigure the formation among AUVs so as to improve the sampling efficiency. The motion-control algorithm allowed the follower vehicles to maintain the desired position relative to the leader vehicle by generating adequate heading and speed commands. [PERSON] et al. [101] proposed a centralised optimisation to reconstruct the given scalar field (temperature) on a two-dimensional horizontal plane. Their solution consisted of two progressive phases: preliminary field estimates were obtained using lawn-mower style sampling in the first phase, then, the field was adaptively sampled in the second phase. The second phase was partitioned into two particular objective functions: the maximum reconstruction error (sub-procedure 1) and the energy consumption (sub-procedure 2). All the samples acquired by the vehicles would be sent to one vehicle. The leader vehicle estimates the scalar field of interest using interpolation and extrapolation. This centralised optimisation may allow a heavy load of the computational complexity on the leader vehicle. A 2D and 3D plume-front-tracking method using a single AUV was proposed by [PERSON] [7] as addressed in Section 4.2. It was extrapolated to increase the spatial coverage using multi-vehicles. In their method, multiple AUVs independently but simultaneously performed two-dimensional zig-zag or three-dimensional helix tracking along the plume boundary. In a 2D zig-zag, all AUVs concurrently followed the leader along the front in a global respect, while each vehicle also performed an adaptive zig-zag motion across the front. In the 3D helix, all AUVs were distributed at different depths along the stratified temperature field. The idea of this collaborative survey was simply to achieve spatial expansion by distributing multiple vehicles to obtain wider coverage, rather than focussing on centralised data assimilation. This multi-scale adaptive approach is suited to large-scale phenomena with less temporal variation. [PERSON] et al. [18] introduced an adaptive sampling strategy for a fleet of AUVs based on coordinated and cooperative control. The central structure of their method is to integrate spatially distributed measurements collected by the sensing network into a coordinated mission planner in real-time. The measurements herein include the georeferencing information defined by GPS and environmental gradient estimates.