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• W3027152079 endingPage "104460" @default.
• W3027152079 startingPage "104460" @default.
• W3027152079 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." @default.
• W3027152079 created "2020-05-29" @default.
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• W3027152079 date "2020-07-01" @default.
• W3027152079 modified "2023-10-16" @default.
• W3027152079 title "Learning control for transmission and navigation with a mobile robot under unknown communication rates" @default.
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• W3027152079 doi "https://doi.org/10.1016/j.conengprac.2020.104460" @default.
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