A mobility-controlled link quality learning protocol for multi-robot coordination tasks

The performance of a team of robots executing a coordination task is, to a large extent, determined by the reliability of the communications between the robots. In wireless networks, one way to improve this reliability is to choose the best among the available wireless links. For this purpose, an accurate link quality model is required. We show how a group of robots can exploit their mobility to effectively and rapidly learn such a model directly from an unknown environment. The LQE (Link Quality Estimation) protocol, which is used by the robots to cooperatively collect labeled link quality samples, and learn out of them, is presented in the paper. The accuracy and the robustness of the LQE approach are validated through a set of real-world experiments, performed with mobile robots operating in different network environments. Moreover, in simulation, we study a multi-robot coordination problem, and show the benefits of using the link quality learning approach, at the expenses of devoting little time for learning the model before executing the task.