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The Right Direction to Smell: Efficient Sensor Planning Strategies for Robot Assisted Gas Tomography
Örebro universitet, Institutionen för naturvetenskap och teknik. (Mobile Robotics & Olfaction (MRO) Lab, Centre for Applied Autonomous Sensor Systems (AASS))ORCID-id: 0000-0002-5973-7424
Örebro universitet, Institutionen för naturvetenskap och teknik. (Mobile Robotics & Olfaction (MRO) Lab, Centre for Applied Autonomous Sensor Systems (AASS))ORCID-id: 0000-0002-0804-8637
Örebro universitet, Institutionen för naturvetenskap och teknik. (Mobile Robotics & Olfaction (MRO) Lab, Centre for Applied Autonomous Sensor Systems (AASS))ORCID-id: 0000-0001-5061-5474
Örebro universitet, Institutionen för naturvetenskap och teknik. (Mobile Robotics & Olfaction (MRO) Lab, Centre for Applied Autonomous Sensor Systems (AASS))ORCID-id: 0000-0003-0217-9326
2016 (engelsk)Inngår i: 2016 IEEE International Conference on Robotics and Automation (ICRA), New York, USA: IEEE Robotics and Automation Society, 2016, s. 4275-4281Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Creating an accurate model of gas emissions is an important task in monitoring and surveillance applications. A promising solution for a range of real-world applications are gas-sensitive mobile robots with spectroscopy-based remote sensors that are used to create a tomographic reconstruction of the gas distribution. The quality of these reconstructions depends crucially on the chosen sensing geometry. In this paper we address the problem of sensor planning by investigating sensing geometries that minimize reconstruction errors, and then formulate an optimization algorithm that chooses sensing configurations accordingly. The algorithm decouples sensor planning for single high concentration regions (hotspots) and subsequently fuses the individual solutions to a global solution consisting of sensing poses and the shortest path between them. The proposed algorithm compares favorably to a template matching technique in a simple simulation and in a real-world experiment. In the latter, we also compare the proposed sensor planning strategy to the sensing strategy of a human expert and find indications that the quality of the reconstructed map is higher with the proposed algorithm.

sted, utgiver, år, opplag, sider
New York, USA: IEEE Robotics and Automation Society, 2016. s. 4275-4281
Emneord [en]
Sensor planning, robot exploration, sensing geometry, robot assisted gas tomography, mobile robot olfaction, coverage planning, surveillance robots
HSV kategori
Forskningsprogram
Datavetenskap
Identifikatorer
URN: urn:nbn:se:oru:diva-50886DOI: 10.1109/ICRA.2016.7487624ISI: 000389516203101Scopus ID: 2-s2.0-84977543569OAI: oai:DiVA.org:oru-50886DiVA, id: diva2:938083
Konferanse
IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden, May 16-21, 2016
Tilgjengelig fra: 2016-06-16 Laget: 2016-06-16 Sist oppdatert: 2017-10-18bibliografisk kontrollert

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