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Global coverage measurement planning strategies for mobile robots equipped with a remote gas sensor
Örebro University, School of Science and Technology. (Mobile Robotics & Olfaction (MRO) Lab, Center of Applied Autonomous Sensor Systems (AASS))ORCID iD: 0000-0002-5973-7424
Örebro University, School of Science and Technology. (Mobile Robotics & Olfaction (MRO) Lab, Center of Applied Autonomous Sensor Systems (AASS))ORCID iD: 0000-0003-0195-2102
Örebro University, School of Science and Technology. (Mobile Robotics & Olfaction (MRO) Lab, Center of Applied Autonomous Sensor Systems (AASS))
Örebro University, School of Science and Technology. (Mobile Robotics & Olfaction (MRO) Lab, Center of Applied Autonomous Sensor Systems (AASS))ORCID iD: 0000-0003-4026-7490
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2015 (English)In: Sensors, ISSN 1424-8220, Vol. 15, no 3, 6845-6871 p.Article in journal (Refereed) Published
Abstract [en]

The problem of gas detection is relevant to many real-world applications, such as leak detection in industrial settings and landfill monitoring. In this paper, we address the problem of gas detection in large areas with a mobile robotic platform equipped with a remote gas sensor. We propose an algorithm that leverages a novel method based on convex relaxation for quickly solving sensor placement problems, and for generating an efficient exploration plan for the robot. To demonstrate the applicability of our method to real-world environments, we performed a large number of experimental trials, both on randomly generated maps and on the map of a real environment. Our approach proves to be highly efficient in terms of computational requirements and to provide nearly-optimal solutions.

Place, publisher, year, edition, pages
Basel, Switzerland: MDPI , 2015. Vol. 15, no 3, 6845-6871 p.
Keyword [en]
Coverage planning; Mobile robot olfaction; Remote gas detection; Sensor planning; Surveillance robots
National Category
Computer Science
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-44407DOI: 10.3390/s150306845ISI: 000354160900112PubMedID: 25803707Scopus ID: 2-s2.0-84928681961OAI: oai:DiVA.org:oru-44407DiVA: diva2:807075
Available from: 2015-04-22 Created: 2015-04-22 Last updated: 2017-10-17Bibliographically approved

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Arain-Sensors2015(15492 kB)143 downloads
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Arain, Muhammad AsifTrincavelli, MarcoCirillo, MarcelloSchaffernicht, ErikLilienthal, Achim J.
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