## 4 Main Issues and Challenges
### Comparisons of the Proposals  
In order to consider the overall assessment that has been done with all the proposals, Table 13 shows the categories for each one according to the criteria that were described in Section 2.  
When looking at Table 13 it is important to recall that a lower category does not necessarily mean a lower achievement at the specific feature. It is normal that different scenarios have different requirements, thus needing different capabilities on the selected features. However, it is quite remarkable that none of the architectures and frameworks evaluated have considered the use of probabilistic planning. Most of the solutions to deal with the unreliable nature of the underwater environment are related to react to unexpected events, either at the vehicle level or at the global mission control level. Continuing with planning, it is interesting that only three of the selected architectures, T-REX, ICA and SWARMs consider the use of multi-agent planning and hierarchical planning, and only T-REX included temporal constraints in the mission plan.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Architectures** & **Plan Specification** & **Adaptation** & **Virtualization** & **Dispatching** \\ \hline T-REX & 2 MHT & 5 & 1 & 3 \\ \hline RAUVI & 2 & 1 & 5 & 2 \\ ICA & 2 MH & 2 & 1 & 1 \\ SWARMs & 2 MH & 4 & 4 & 4 \\ CPEF & 1 & 3 & 1 & 3 \\ TRAC & 1 & 5 & 1 & 3 \\ ROSPlan & 3 & 1 & 1 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Comparisons of categories assigned to the reviewed architectures and frameworks.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **Category** & **Explanation** \\ \hline Plan & 3 & All the plans managed by ROSPlan are described using PDDL. \\ Specification & 1 & There is no mention to any self-adaption capabilities provided by ROSPlan, and none can be identified from the proposal description. \\ Virtualization & 1 & There is no mention to any kid of virtualization or abstraction of the vehicles \\ \hline \multirow{3}{*}{Dispatching} & \multirow{3}{*}{3} & The actions are dispatched one by one in a sequential way. The Plan Dispatch node just returns an error if a failure occurs while execution a plan, or a success message if all the actions in the plan are successfully executed. \\ \cline{3-3}  & & \\ \hline \hline \end{tabular}
\end{table}
Table 12: ROSPlan assessment.  
The same can be said about the virtualization of the vehicles from the point of view of mission management. Only two of the presented architectures, RAUVI and SWARMs, defined some mechanism to virtualize or abstract the vehicles capabilities. And this can be a potential issue to adapt the architecture to work with new vehicles with different capabilities.  
Regarding the adaptation, there is greater diversity. TRAC describes an adaptation mechanism where the information is shared among all the agents in the architecture and the adaptation is performed using a decentralized control pattern. T-REX also mentions the use of a coordination control using self-adaptive loops at the reactors level. SWARMs also share information among the agents, but the decision to adapt is taken independently by each agent. In CPEF, the adaptation of the mission is only mentioned at the plan management level, while in ICA it is only mentioned at the vehicle level. Finally, RAUVI and ROSPlan do not mention adaptation at all, and the use of adaptation models cannot be inferred from their description.  
Finally, regarding the dispatching of the mission plan to the vehicles, there is a great variance for the architectures designed for specific use of underwater vehicles (T-REX, RAUVI, ICA and SWARMs). On the other hand, CPEF, TRAC and ROSPlan all use a task-by-task dispatch, possibly because they are not considering the constrained nature of underwater communications. Further, only SWARMs described the possibility of using either a task-by-task or a full mission dispatch from the global mission manager.