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A dynamic safety system based on sensor fusion
Linköping University, Department of Physics, Chemistry and Biology. Linköping University, The Institute of Technology.
Defence Research Establishment, Linköping.
Örebro University.
2000 (English)In: Journal of Intelligent Manufacturing, ISSN 0956-5515, Vol. 11, no 5, 475-483 p.Article in journal (Refereed) Published
Abstract [en]

Machines in industry, including industrial robots, have in many cases dramatically reduced the man-made work and improved the work environment. New machines introduce, however, new risk factors. Traditionally machines are safeguarded by means that more or less rigidly separates the machines from the personnel. This works well in many traditional areas, i.e., where industrial robots are involved. There is however a risk that the safety system limits the valuable flexibility of the robot, which can be considered as a quality that tends to become even more valuable in the progress of programming possibilities and sensor technology. This article shows an example how a safety system can be designed to achieve increased flexibility in co-operation between human and production safety strategy. The proposed safety system is totally based on sensor information that monitors the working area, calculate the safety level and improve the system dynamically, e.g., reduce the robot capability in conjunction to the system safety level. The safety system gain information from the sensors and calculates a risk level which controls the robot speed, i.e., the speed is reduced to achieve a sufficiently low risk level. The sensor data is combined with fuzzy-based sensor fusion and fuzzy rules. The safety system is based on sensor information, hence it automatically adjusts to changes in the guarded area as long as the functionality of the sensors is maintained. Finally, we present a system implementation in an industrial robot application.

Place, publisher, year, edition, pages
2000. Vol. 11, no 5, 475-483 p.
Keyword [en]
safety system; sensors; sensor data fusion; fuzzy logic
National Category
Engineering and Technology
URN: urn:nbn:se:liu:diva-53570DOI: 10.1023/A:1008922330419OAI: diva2:289377
Available from: 2010-01-25 Created: 2010-01-25 Last updated: 2010-01-25

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Department of Physics, Chemistry and BiologyThe Institute of Technology
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