# Proposal of an Automated Mission Manager for Cooperative Autonomous Underwater Vehicles
## 1 Introduction  
The use of autonomous underwater vehicles (AUVs) for marine interventions has attracted increasing interest in recent years from ocean scientists, marine industries and the military [1; 2; 3]. However, the use of this kind of robotic system for underwater operations faces a series of challenges that include: the power required to operate them, the constrained nature of underwater communications, the limited perception capabilities of the aforementioned systems, the lack of an underwater positioning system, and the reduced scope of existing underwater maps and the need for autonomous adaption to achieve a certain degree of delegation [1]. This latter delegation challenge has usually been tackled by defining a sequence of tasks assigned to an AUV, and known as a mission plan [4; 5]. In the early uses of AUVs, the mission plan was typically created manually by human operators, being replaced recently by the use of deterministic planning algorithms [3]. The full mission plan is typically loaded into the AUVs to be launched, either from an onshore facility or a support vessel. Once loaded into each AUV, the mission plan is usually referred to as a vehicle plan (for single AUV missions it may still be called the mission plan). Then the AUV is transferred to the launching point, and once it is deployed on the sea surface, it is kept idle until the reception of the start command to begin the mission [2; 6]. The AUV is then responsible for executing the tasks in the pre-loaded vehicle plan in the given order. In consequence, when an event that prevents the execution of a given task occurs, the plan is considered as broken. The most basic AUVs will then abort the mission, and use fail-safe mechanisms to navigate to a previously defined recovery location. Most up-to-date AUVs can improve the response by including an onboard planner that is capable of performing some adaptive planning techniques [7; 8], like trying to repair the plan [9; 10; 11], or just making a new one [10]. This behavior, that can be part of a subsumption approach [12], introduces the use of self-adaptive models at the vehicle level, like the Observe, Orient, Decide, Act (OODA) loop [13] used in the Intelligent Control Architecture (ICA) proposal [14; 15], the Sense-Plan-Act [12] that inspired the Teleo-Reactive EXecutive architecture (T-REX) proposal [16; 17], the Event-Condition-Action [18], and the autonomic manager model [18; 19], better known as MAPE-K due to its four functional parts (Monitor, Analyze, Plan, Execute), and the use of shared knowledge among them. However, exogenous events beyond the control of the vehicles can affect the preconditions of some tasks in the plan, or even produce a deviation in the outcome of a running action [20]. In these cases, even the self-adaptive capabilities of the AUVs may not be enough to solve the situation. Thus, the vehicle plan must still be aborted, and when a mission uses multiple AUVs, this can lead to aborting the full mission plan. In the literature reviewed in Section 2 we have found that once the mission plan has been loaded into the vehicles, the mission management is usually considered only at the vehicle level. This means that when a vehicle plan fails, the rest of the AUVs participating in a multiple-vehicle mission remain oblivious to the problem. Adding a mission manager at an upper layer can be useful for handling these situations in multiple vehicle missions. Another issue that we have found is that typically each proposal is aimed at the use of a specific vehicle or a set of vehicles with the same capabilities regarding the supervision and execution of a vehicle plan. This implies that there is no consideration for the reuse of older AUVs with fewer capabilities. The use of a virtualization model within the upper layer mission manager can enable the integration of legacy systems in new missions. In summary, the issues introduced in the previous paragraphs indicate a research gap with regards to the following statements:  
1. Adaptation is mostly delegated to the vehicles.