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The Right Direction to Smell: Efficient Sensor Planning Strategies for Robot Assisted Gas Tomography
Örebro University, School of Science and Technology. (Mobile Robotics & Olfaction (MRO) Lab, Centre for Applied Autonomous Sensor Systems (AASS))ORCID iD: 0000-0002-5973-7424
Örebro University, School of Science and Technology. (Mobile Robotics & Olfaction (MRO) Lab, Centre for Applied Autonomous Sensor Systems (AASS))ORCID iD: 0000-0002-0804-8637
Örebro University, School of Science and Technology. (Mobile Robotics & Olfaction (MRO) Lab, Centre for Applied Autonomous Sensor Systems (AASS))ORCID iD: 0000-0001-5061-5474
Örebro University, School of Science and Technology. (Mobile Robotics & Olfaction (MRO) Lab, Centre for Applied Autonomous Sensor Systems (AASS))ORCID iD: 0000-0003-0217-9326
2016 (English)In: 2016 IEEE International Conference on Robotics and Automation (ICRA), New York, USA: IEEE Robotics and Automation Society, 2016, p. 4275-4281Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
New York, USA: IEEE Robotics and Automation Society, 2016. p. 4275-4281
Keywords [en]
Sensor planning, robot exploration, sensing geometry, robot assisted gas tomography, mobile robot olfaction, coverage planning, surveillance robots
National Category
Robotics Computer Systems
Research subject
Computer Science
Identifiers
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
Conference
IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden, May 16-21, 2016
Available from: 2016-06-16 Created: 2016-06-16 Last updated: 2022-08-09Bibliographically approved
In thesis
1. Efficient Remote Gas Inspection with an Autonomous Mobile Robot
Open this publication in new window or tab >>Efficient Remote Gas Inspection with an Autonomous Mobile Robot
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Human-caused greenhouse gas emissions are one of the major sources of global warming, which is threatening to reach a tipping point. Inspection systems that can provide direct information about critical factors causing global warming, such as systems for gas detection and location of gas sources, are urgently needed to analyze the fugitive emissions and take necessary actions.

This thesis presents an autonomous robotic system capable of performing efficient exploration by selecting informative sampling positions for gas detection and gas distribution mapping – the Autonomous Remote Methane Explorer (ARMEx). In the design choice of ARMEx, a ground robot carries a spectroscopybased remote gas sensor, such as a Remote Methane Leak Detector (RMLD), that collects integral gas measurements along up to 30 m long optical-beams. The sensor is actuated to sample a large area inside an adjustable field of view, and with the mobility of the robot, adaptive sampling for high spatial resolution in the areas of interest is made possible to inspect large environments.

In a typical gas sampling mission, the robot needs to localize itself and plan a traveling path to visit different locations in the area, which is a largely solved problem. However, the state-of-the-art prior to this thesis fell short of providing the capability to select informative sampling positions autonomously. This thesis introduces efficient measurement strategies to bring autonomy to mobile remote gas sensing. The strategies are based on sensor planning algorithms that minimize the number of measurements and distance traveled while optimizing the inspection criteria: full sensing coverage of the area for gas detection, and suitably overlapping sensing coverage of different viewpoints around areas of interest for gas distribution mapping.

A prototype implementation of ARMEx was deployed in a large, real-world environment where inspection missions performed by the autonomous system were compared with runs teleoperated by human experts. In six experimental trials, the autonomous system created better gas maps, located more gas sources correctly, and provided better sensing coverage with fewer sensing positions than human experts.

Place, publisher, year, edition, pages
Örebro: Örebro University, 2020. p. 78
Series
Örebro Studies in Technology, ISSN 1650-8580 ; 88
Keywords
environmental monitoring, measurement planning, remote gas sensing, mobile robot olfaction, service robots
National Category
Computer Sciences
Identifiers
urn:nbn:se:oru:diva-87393 (URN)978-91-7529-344-8 (ISBN)
Public defence
2020-12-18, Örebro universitet, Forumhuset, Hörsal F, Fakultetsgatan 1, Örebro, 14:00 (English)
Opponent
Supervisors
Available from: 2020-11-16 Created: 2020-11-16 Last updated: 2024-01-03Bibliographically approved

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Arain, Muhammad AsifSchaffernicht, ErikHernandez Bennetts, VictorLilienthal, Achim J.

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