## 4 Conclusions  
A review of different alternatives for underwater localization, communication, and navigation of Autonomous Underwater Vehicles is addressed in this work. Although Section 2 discusses single-vehicle localization and navigation, the aim of this work is to show that those technologies are being applied to multi-vehicles systems, or can be implemented in the future. Every underwater mission is different and has its own limitations and challenges. For that reason, it is not possible to state which localization or navigation system has the best performance. For a long-range mission (kilometers) an accuracy of tens or hundreds of meters from an inertial-based navigation system can be acceptable, as the main characteristic wanted for it is the long-range capacity. In a small navigation environment, i.e., a docking station or a laboratory tank, there is a need for much better accuracy to avoid collisions with the tank's walls. In that situation, an SBL/USBL system at an operation frequency of 200 kHz can perform with errors in centimeters range or, if the water conditions are favorable, a visual-based system can perform even better at a lower cost.  
Current achievements in the field of collaborative AUVs have been also presented, including communication, collaborative localization and navigation, surveillance, and intervention missions. The use of a hybrid (acoustic and light-based) system is a promising option for the communication of collaborative AUVs. The acoustic sub-system can handle the long-range communications meanwhile the light-based takes care of the inter-vehicle communication where a high rate is critical for collision avoidance. A hybrid system can be an interesting alternative also for collaborative navigation. Acoustic methods can be implemented in a team of AUVs for medium/long-range navigation meanwhile a visual-based method is used to maintain the formation and avoid collisions between the vehicles. In terms of algorithms, machine learning seems to be one of the best approaches to achieve collaborative navigation and to give a team the capacity to perform complex surveillance and intervention missions. Relating to collaborative intervention missions, which mostly have been addressed with numerical simulations, the next step is to test the algorithms in real experiments. Such experimentation can be done in controlled conditions, such as a laboratory tank, where the vehicles would not have to dealwith the changing conditions of the sea, so researches can focus on the collaborative task algorithms such as the carrying of an object by two vehicles.  
Conceptualization, [PERSON], [PERSON], [PERSON], and [PERSON]; methodology, [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON]; investigation, [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, all authors; visualization, [PERSON]; supervision, [PERSON], [PERSON], [PERSON], and [PERSON]; funding acquisition J.A.E.C.; project administration, [PERSON], [PERSON], [PERSON], and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
The authors would like to acknowledge the financial support of Tecnologico de Monterrey, in the production of this work.  
The authors would like to acknowledge support from CONACyT for PhD studies of first author (scholarship number 741738).  
The authors declare no conflict of interest.