Learning control for transmission and navigation with a mobile robot under unknown communication rates
Abstract
In tasks such as surveying or monitoring remote regions, an autonomous robot must move while transmitting data over a wireless network with unknown, position-dependent transmission rates. For such a robot, this paper considers the problem of transmitting a data buffer in minimum time, while possibly also navigating towards a goal position. Two approaches are proposed, each consisting of a machine-learning component that estimates the rate function from samples; and of an optimal-control component that moves the robot given the current rate function estimate. Simple obstacle avoidance is performed for the case without a goal position. In extensive simulations, these methods achieve competitive performance compared to known-rate and unknown-rate baselines. A real indoor experiment is provided in which a Parrot AR.Drone 2 successfully learns to transmit the buffer.
Cite
@article{arxiv.2011.09193,
title = {Learning control for transmission and navigation with a mobile robot under unknown communication rates},
author = {L. Busoniu and V. S. Varma and J. Loheac and A. Codrean and O. Stefan and I. -C. Morarescu and S. Lasaulce},
journal= {arXiv preprint arXiv:2011.09193},
year = {2020}
}
Comments
Control Engineering Practice, Elsevier, Vol. 100, July 2020, 104460