*7.1. Task Assessment Problem*

The proposed *Auto-Adaptive Multi-Objective (AAMO)* approach follows a multi-objective assessment strategy where the tasks under consideration are assessed regarding two objectives: the cost associated with the corresponding shortest path and the connectivity level each task location can offer to robots at arrival time. The multi-objective strategy is implemented employing a weighted sum that trades travelling cost off for connectivity levels. Up until now, all these concepts are quite standard being present in several state-of-art approaches.

Nevertheless, in this work: (i) Connectivity awareness ability is given to the robots by modelling attenuation effects that commonly affect the communication signal strength; (ii) the weights of these potentially conflicting objectives are derived from formal analysis instead of a training stage, making the system more adaptable to different environments; (iii) The human operator is asked to use his application-field expertise to play a part in the task assessment process by setting a distance threshold until which the tasks that preserve or enlarge connectivity are preferred over the rest. All this leads to a more flexible system where the robots can deal with communication constraints adjusting the weights of each objective independently of any scenario in a more intuitive manner, saving a lot of training time.

All existence and correctness proofs conducted on the task selection procedure support the fact that the robots are always capable of auto-adapting the objectives weights in order to select the tasks accordingly with the human-operator criterion. In conclusion, this task assessment approach may be very advantageous considering its ease of deployment.
