Learning control for transmission and navigation with a mobile robot under unknown communication rates

TL;DR

This paper develops machine learning and optimal control methods enabling a mobile robot to efficiently transmit data and navigate in environments with unknown, position-dependent wireless communication rates, demonstrated through simulations and real-world experiments.

Contribution

It introduces a novel approach combining rate estimation and optimal control for transmission and navigation under unknown communication conditions.

Findings

Methods achieve competitive performance in simulations.

Real indoor experiment validates the approach.

Effective data transmission and navigation in unknown environments.

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.