# AUV Adaptive Sampling Methods: A Review
## 4 Mission Objectives of Sampling
### Front and Boundary Determination
[PERSON] [7] developed a real-time adaptive approach combining the methods introduced by [PERSON] [89] and [PERSON] et al. [90] for tracking an upwelling front. He suggested two methods: a two-dimensional survey with a zigzag boundary tracking and a three-dimensional survey with a horizontal helix tracking. The isotherm was used as a robust proxy to indicate the front. The AUV collected the temperature data while constantly updating the geolocation estimates. The AUV adapted its path and motion to track the front and to sample along the boundary with spatial and temporal variance. The two-dimensional front tracking with constant depth method consists of three phases: Detection, Classification and Tracking. The maximum and minimum temperatures are recorded in real-time by the AUV. The isotherm of the mean temperature of the measured maximum and minimum was used to identify the front. As a result, the AUV is able to detect the front while following a pre-planned loiter or spiral pattern. When three accumulative crossing points are detected, the algorithm moves on to the next phase. In the classification phase, a weighted linear least squares approximation, based on the three crossing points, was used to classify the local frontal line. Once it was classified, the AUV heading was adapted to cross the front line that is denoted by the isotherm at a 45\({}^{\circ}\) angle. After travelling for a specified distance, the AUV was commanded to cross back to the front line at a 45\({}^{\circ}\) angle, again resulting in a zigzag pattern along the front. This adaptive tracking method enables the vehicle to continue to track a newly updated front line in space and time. Three-dimensional front tracking is an extrapolated version of the two-dimensional method. The significance of this method is the use of the isotherm, as opposed to the temperature gradient that requires an appropriate threshold for successful detection. This reduces the complexity of implementation and calculation process after measurement as temperatures can be directly measurable and readily usable values. However, there is an inevitable concern in targeting the isotherm. The vehicle might fail to go beyond the confine of a local temperature pocket that has an identical isothermal level with the ocean front. This should be scrupulously avoided with additional boundary conditions. [PERSON] et al. [73] developed an area-based plume front-tracking method for oil propagation based on an onboard sensor payload. The individual process connected with a communication structure within their proposed control algorithm architecture is described below. 1. Flow Field: A probabilistic Lagrangian environmental model was used to simulate the changes of oil in contact with water. The spilled oil immediately undergoes a variety of physical and chemical processes including advection, evaporation, emulsification, oxidation and degradation [93]. Among these, the most significant transporting factors, advection and diffusion were considered. The Navier-Stokes equations were used to generate the flow field. 2. Environment: Chemical (oil) concentration, gradient of concentration and divergence of the concentration were accordingly generated. 3. Main Simulation: This process received the current position of the vehicle, then passed it onto the Environment processing block. After receiving the chemical concentration data, the combined information was transferred to the Observer process. 4. Observer: Based on the observed plume front location received from the Main Simulation, the vehicle control inputs were generated to feed the next process. 5. Vehicle control: The updated robot position and orientation were calculated, and this information was sent back to the Main Simulation. In a simulation, the algorithm was found to successfully estimate the front of a simulated oil plume and track the varying location of the boundary. However, it was designed to work well with area-based detection methods such as infrared or visible spectrum detection methods.