*7.3. Connectivity Maintenance Problem*

While all event-based connectivity approaches consist in the execution of regaining-connectivity actions in the presence of specific events (e.g., typically disconnections, whenever it happens or periodically after a certain amount of time), the *AAMO* approach integrates a less restrictive connectivity strategy where the robots are motivated but not compelled to regain connectivity. When selecting their targets, the robots are always considering the opportunity cost of keeping connected or regaining connectivity, implicitly. Furthermore, in reconnection cases, the task location becomes the meeting point itself eliminating the need for rendezvous policy implementation and, maybe more important, avoiding deviations from natural paths. This way, the policy is utterly transparent to the eyes of the external observer: every time it is possible to explore and keep or enlarge connectivity level the robots will choose this option. On the contrary, when it is not, they merely behave guided by a pure path-cost exploration.

Particularly concerning the efficiency related to both completion time and connectivity level maintenance, the approach is capable of decreasing the last of disconnection periods without a noticeable degradation of the completion exploration time, appearing as an intermediate solution that presents much better completion time performance than the most restrictive event-based connectivity approaches and better connectivity level along exploration than the approaches that do not take care about communication issues.

#### *7.4. Future Research Directions*

New research questions have arisen along this work leaving, as a result, several opportunities to improve the developed system. Although the environments employed in simulations are proposed as benchmarks, it would be beneficial to check the validity and performance of the proposed approach on a broader variety of scenarios. Large office-like environments would be exciting to put the system into more realistic situations like mapping buildings where larger fleets could be employed, too. Since the robot model defined can support robots with different characteristics, exploiting heterogeneity could be a promising research direction. Integrating a fleet with heterogeneous robots (e.g., different in size, sensory, and motion capabilities) could enhance the skills of the fleet. For instance, given their greater mobility, UAVs could help the fleet to keep connected by playing a relay role, while small terrestrial robots could be the key to get into access-restricted spaces. At last but not least, executions on real systems are also planned. Despite the goodness of any simulator, many important details escape their scopes. The proposed approach is designed to serve as a solution for real-world applications so that it is imperative to verify its feasibility in real scenarios. In such a case, localisation and mapping errors cannot be ignored. Both simultaneous Localisation and Mapping (SLAM) algorithms and the sensory and motor devices should be carefully studied to limit the influence of this kind of errors on the high-level decision components. Regarding the equipment availability of the involved laboratories, the candidate platforms would be either IRobot or KheperaIII units.

**Author Contributions:** Conceptualisation, Methodology, Software, Validation, Analysis and Writing-original draft preparation, F.B.; Writing reviews and Supervision, E.G., C.C. and P.M.

**Funding:** This research was partially funded by the Comisión Sectorial de Investigación (CSIC-UdelaR) through its mobility program MIA.

**Conflicts of Interest:** The authors declare no conflict of interest.
