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

• URL: http://arxiv.org/abs/2011.09193v1
• Date: Wed, 18 Nov 2020 10:26:15 GMT
• Title: Learning control for transmission and navigation with a mobile robot under unknown communication rates
• Abstract summary: In tasks such as surveying or monitoring remote regions, an autonomous robot must move while transmitting data over a wireless network. 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. In extensive simulations, these methods achieve competitive performance compared to known-rate and unknown-rate baselines.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• 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.

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