Learning control for transmission and navigation with a mobile robot under unknown communication rates
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...
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| Vydáno v: | Control engineering practice Ročník 100; s. 104460 |
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| Hlavní autoři: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Ltd
01.07.2020
Elsevier |
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| ISSN: | 0967-0661, 1873-6939 |
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| 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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| AbstractList | 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. |
| ArticleNumber | 104460 |
| Author | Codrean, Alexandru Lasaulce, Samson Lohéac, Jérôme Varma, Vineeth S. Buşoniu, Lucian Morărescu, Irinel-Constantin Ştefan, Octavian |
| Author_xml | – sequence: 1 givenname: Lucian surname: Buşoniu fullname: Buşoniu, Lucian email: lucian@busoniu.net organization: Technical University of Cluj-Napoca, Memorandumului 28, 400114 Cluj-Napoca, Romania – sequence: 2 givenname: Vineeth S. surname: Varma fullname: Varma, Vineeth S. email: vineeth.varma@univ-lorraine.fr organization: Université de Lorraine, CNRS, CRAN, F-54000 Nancy, France – sequence: 3 givenname: Jérôme surname: Lohéac fullname: Lohéac, Jérôme email: jerome.loheac@univ-lorraine.fr organization: Université de Lorraine, CNRS, CRAN, F-54000 Nancy, France – sequence: 4 givenname: Alexandru surname: Codrean fullname: Codrean, Alexandru email: alexandru.codrean@aut.utcluj.ro organization: Technical University of Cluj-Napoca, Memorandumului 28, 400114 Cluj-Napoca, Romania – sequence: 5 givenname: Octavian surname: Ştefan fullname: Ştefan, Octavian email: octavian.stefan@aut.upt.ro organization: Automation and Applied Informatics, Politehnica University of Timisoara, Romania – sequence: 6 givenname: Irinel-Constantin surname: Morărescu fullname: Morărescu, Irinel-Constantin email: constantin.morarescu@univ-lorraine.fr organization: Université de Lorraine, CNRS, CRAN, F-54000 Nancy, France – sequence: 7 givenname: Samson surname: Lasaulce fullname: Lasaulce, Samson email: samson.lasaulce@centralesupelec.fr organization: Université de Lorraine, CNRS, CRAN, F-54000 Nancy, France |
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| Cites_doi | 10.1023/A:1022635613229 10.1007/s11036-008-0150-5 10.1109/JPROC.2011.2161427 10.1109/ACCESS.2013.2262013 10.1109/TWC.2013.021213.120138 10.1038/nature14236 10.1016/j.ins.2018.09.060 10.1109/WINCOM.2016.7777225 10.1177/0278364913501779 10.1109/TAC.2011.2164033 10.23919/ACC.2019.8815213 10.1016/j.conengprac.2006.08.007 10.1109/JPROC.2006.887293 10.1109/TAP.2019.2925068 10.1016/B978-1-55860-141-3.50030-4 10.1109/CDC.2012.6426683 10.1016/j.automatica.2010.02.006 10.1023/A:1022628806385 |
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| Keywords | Wireless communication Learning control Mobile robots |
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