Good Performance

Asynchronism is taken as a natural way of avoiding waiting times to make a decision as well as decreasing the number of robots that are simultaneously making a decision. Since the task allocation computation strongly depends on the number of robots under consideration, asynchronism also makes optimal decisions can be linearly computable most of the time. As a consequence, robots can compute optimal tasks-to-robots distributions in a short time, achieving high levels of dispersion efficiently. Besides, regarding reconnections, the proposal consists of a rendezvous policy where the locations of the selected tasks become the meeting points themselves, avoiding deviations from the planned paths. Compared with others, the proposed approaches are capable of decreasing the last of disconnection periods without noticeable degradation of the completion exploration time.
