## 3 Collaborative AUVs
### Collaborative Missions
#### 3.3.1 Search Missions  
A good search mission needs to minimize the number of vehicles required and maximize the efficiency of the search. Oceanic, biologic, and geologic variability of underwater environments impact in the search performance of teams of AUVs. To address search planning in these conditions, where the detection process is prone to false alarms, [PERSON] et al. [135] applied a game theoretic approach to the optimization of a search channel characterization of the environment. The search space is partitioned into discrete cells in which objects of interest may be found. The game theory approach seeks to find the equilibrium solution of the game rather than the optimal solution to a  
Figure 17: Collaborative missions for AUVs. (**a**) Cooperative inspection of subsea formations. (**b**) Cooperative object recovery.  
fixed objective of maximizing the value-over-cost. To demonstrate effectiveness in achieving the game objective, a sequence of searches by four search agents over a search region was planned and simulated. [PERSON] et al. [136] proposed a sub-region collaborative search strategy and a target searching algorithm based on a perceptual adaptive dynamic prediction. The reality of the local environment is obtained by using the FLS of the multi-AUV system. The simulation experiments verified that the algorithm proposed successfully searched and tracked the target. Moreover, in the case of an AUV failure, it can also ensure that other AUVs cooperate to complete the remaining target search tasks. Algorithms for collaborative search based on Neural Networks (NN) are being designed to overcome the variability of the environment and the presence of obstacles. [PERSON] et al. [137] presented a region search algorithm based on a Glasius Bio-inspired Neural Network (GBNN), which can be used for AUVs to perform target search tasks in underwater regions with obstacles. In this algorithm, the search area is divided into several discrete sub-areas and connections are made between adjacent neurons. AUVs and obstacles are introduced to the network as sources of excitation in order to avoid collisions during the search process. By constructing hypothetical targets and introducing them into the NN as stimulating sources of excitation, the AUVs are guided to quickly search for areas where the target is likely to exist and they can efficiently complete the search task. [PERSON] et al. [138] designed a new strategy for collaborative search with a GBNN algorithm. In the algorithm, a grid map is set up to represent the working environment and NN are constructed where each AUV corresponds to a NN. All the AUVs must share information about the environment and, to avoid collision between the vehicles, each AUV is treated as a moving obstacle in the region. Simulation was conducted in MATLAB to confirm that through the proposed algorithm, multi-AUVs can plan reasonable and collision-free coverage path and reach full coverage on the same task area with division of labor and cooperation. [PERSON] et al. [139] addressed a control problem for a group of AUVs tracking a moving target with varying velocity. For this algorithm, at least one AUV is assumed to be capable of obtaining information about the target, and the communication topology graph of the vehicle is assumed to be undirected connected. Simulations were made using MATLAB to demonstrate the efficiency and effectiveness of the proposed control algorithm, considering a system with three vehicles.