Learning control for transmission and navigation with a mobile robot under unknown communication rates
This work is significant for autonomous robots operating in remote regions, enabling efficient data transmission and navigation despite unknown communication conditions, which is an incremental improvement for mobile robotics.
This paper addresses the problem of a mobile robot transmitting a data buffer in minimum time while navigating to a goal position under unknown, position-dependent wireless communication rates. The proposed methods combine machine learning for rate estimation with optimal control for robot movement, achieving competitive performance against known-rate and unknown-rate baselines in simulations and demonstrating successful buffer transmission in a real indoor experiment.
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.