# AUV Adaptive Sampling Methods: A Review
## 3 Target of Interest
### Chemical Features
#### Chemical Plumes  
Ex-situ information has inevitable shortcomings such as sample transport offsite, labour-intensive processes, and emerging errors induced by physical and chemical changes such as degradation during transport. [PERSON] et al. [60] highlighted the benefits of in-situ sensing control to overcome such drawbacks. They explicated the necessity of payload sensors and navigation sensor integration for adaptive surveying by comparing the results of non-adaptive sampling and adaptive sampling. The non-adaptive mission was to detect dissolved methane from the ocean floor at a depth of 100 to 150 m near the Atlantic Coast of North Carolina and Virginia. A Seabed AUV [61] was integrated with a flow-through pumping system around the bow for chemical detection by a methane sensor (METS). The mission commanded the AUV to conduct a bottom-following behaviour to maintain a 3 m altitude in order to try to identify a methane seepage. A chemical anomaly in methane concentration range was set as the sampling proxy. The second mission was to perform an adaptive behaviour triggered by a dissolved propane concentration of 15 nm. The point source of a plume of propane gas was located at a depth of 16 m, while the Seabed AUV was programmed to maintain a constant depth of 7 m. The two-dimensional propane plume was subjected to advection by tidal currents. The AUV initially followed a rhombus trajectory until the vehicle entered the plume and met the threshold. Then, the vehicle performed an adaptive grid behaviour by conducting a small-scale exhaustive grid survey within the confined region until the concentration went below the threshold. This trigger-based approach works effectively only if an appropriate target concentration level (using a threshold) is known. A possible alternative can be to set dynamic parameters such as the gradient of the concentration in comparison with the background levels. Observing the chemical gradient, the vehicle manoeuvres or the trajectory can be modified accordingly.  
[PERSON] et al. [62] developed a behaviour-based plan (BBP) strategy for tracking a chemical plume and locating a stationary source. Their approach was inspired from the olfactory mechanisms that are frequently found in a variety of biological behaviours in nature. Unlike the recommendation suggested by [PERSON] [60], they pointed out the inadequacy of utilising the gradient method (calculating the chemical concentration gradient) in cases of source seeking in a turbulent flow. Most chemical plumes are apt to disperse predominantly by external forces, such as ambient currents, resulting in molecular diffusion. In the presence of turbulent diffusion, the chemical plume is likely to exhibit an intermittent patchy distribution, which makes it ineffective for the vehicle to head toward the source of emission by following the increasing gradient of the concentration of a parameter. Ultimately, they developed a set of reactive behaviour modules. Each module was switched and triggered based on the obtained information with respect to the vehicle location, fluid flow and chemical concentration. The assumption underlying their approach is that the chemical plume can be approximated as a two-dimensional layer with a limited vertical extent. Allowing the benefit of computational simplification, this efficiently represents a realistic buoyant plume. The architecture of their reactive algorithm modules from the perspective of source seeking is further explained in Section 4.1.