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A dynamic safety system based on sensor fusion

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Abstract

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.

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References

  • Dhillon, B. S. and Fashandi, A. R. M. (1997) Safety and reliability assessment techniques in robotics. Robotica 15, 701-708.

    Google Scholar 

  • European Standard EN 775. Safety of Machinery-Manipulating Industrial Robots-Safety. 05/93, CEN, Brussels.

  • Graham, J. H. (1995) A Fuzzy logic approach for safety and collision avoidance in robotic systems. The International Journal of Human Factors in Manufacturing, 5(3), 447-457.

    Google Scholar 

  • Karlsson, B. (1998) Fuzzy measures for sensor data fusion in industrial recycling, Meas. Sci. Technol., 9, 907-912.

    Google Scholar 

  • Karlsson, B. and Fugger, E. (1998) Automatized disassembly of electrical industrial motors, Proc. SPIE Conf. Intelligent Systems in Design and Manufacturing, Boston, MA, USA, November 2-5, pp. 303-312.

  • Karlsson, N. (1999) A capacitance sensor for safeguarding operators of industrial robots. Robotica, 17, 33-39.

    Google Scholar 

  • Karlsson, N. and JaÈrrhed, J.-O. (1993) A capacitive sensor for the detection of humans in a robot cell, Proceedings of the IEEE Instrumentation and Measurement Technology Conference, May 18-20, IEEE-IMTC/93.

  • Kilmer, R. D. (1985) Safety sensor system, in Robot Safety, (eds.) M. C. Bonney and Y. F. Yong, IFS Publ. Ltd. (Int. trends in manufacturing technology) Springer-Verlag, Berlin, pp. 223-235.

    Google Scholar 

  • Kuivanen, R. and Karwowski, W. (1992) Experimental study to determine safe limit for reduced speed of robot motions, in Ergonomics of Hybrid Automated Systems III, (eds.) P. Broèdner and W. Karwowski, Amsterdam, Elsevier, pp. 475-80.

    Google Scholar 

  • Malm, T. (1991) Optical proximity switch as a safety sensor. Reliability and safety of Processes and Manufacturing Systems. Proceedings of the 12th Annual Symposium of the Society of Reliability Engineers, Scandinavian Chapter, pp. 190-99.

  • Mauris, G., Lasserre, V. and Foulloy, L. (1998) Fuzzy modelling of measurements acquired by an intelligent ultrasonic telemeter. Proceedings of the IEEE Instrumentation and Measurement Technology Conference, IEEE-IMTC/98, St. Paul, Minnesota, USA, May 18-21, pp. 837-842.

  • Odeberg, H. (1994) Fusing sensor information using fuzzy measures. Robotica 12, 465-472, Cambridge University Press.

    Google Scholar 

  • Sugimoto, N. and Kawaguchi, K. (1983) Fault-tree analysis of hazards created by robots, Proceedings of the 13th International Symposium on Industrial Robots and Robots 7, Chicago, USA, April, pp. 9.18-9.28.

  • Yamada, Y., Kazutsugu, S., Imai, K., Ikeda, H. and Sugimoto, N. (1996) A failure-to-safety robot system for human-robot coexistence. Robotics and Autonomous Systems, 18, 283-291.

    Google Scholar 

  • Zadeh, L. (1965) Fuzzy sets. Information and Control, 8, 338-353.

    Google Scholar 

  • Zurada, J. and Graham, J. H. (1995) Sensory integration in a neural network-based robot safety system. The International Journal of Human Factors in Manufacturing, 5(3), 325-340, John Wiley & Sons Inc.

    Google Scholar 

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Karlsson, B., Karlsson, N. & Wide, P. A dynamic safety system based on sensor fusion. Journal of Intelligent Manufacturing 11, 475–483 (2000). https://doi.org/10.1023/A:1008922330419

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