Hostname: page-component-8448b6f56d-mp689 Total loading time: 0 Render date: 2024-04-24T17:46:27.043Z Has data issue: false hasContentIssue false
  • English
  • Français

Human - robot collision detection and identification based on fuzzy and time series modelling

Published online by Cambridge University Press:  09 May 2014

Fotios Dimeas ,
L. D. Avendaño-Valencia  and
Nikos Aspragathos
Fotios Dimeas*
Affiliation:
Department of Mechanical Engineering & Aeronautics, Robotics Group, University of Patras, Greece
L. D. Avendaño-Valencia
Affiliation:
Department of Mechanical Engineering & Aeronautics, Stochastic Mechanical Systems & Automation Lab, University of Patras, Greece
Nikos Aspragathos
Affiliation:
Department of Mechanical Engineering & Aeronautics, Robotics Group, University of Patras, Greece
*
*Corresponding author. E-mail: dimeasf@mech.upatras.gr
Get access Rights & Permissions [Opens in a new window]

Summary

In this paper, two methods are proposed and implemented for collision detection between the robot and a human based on fuzzy identification and time series modelling. Both methods include a collision detection system for each joint of the robot that is trained to approximate the external torque. In addition, the proposed methods are able to detect the occurrence of a collision, the link that collided and to some extent the magnitude of the collision without using the explicit model of the robot. Since the speed of the detection is of critical importance for mitigating the danger, attention is paid to recognise a collision as soon as possible. Experimental results conducted with a KUKALWR manipulator using two joints in planar motion, verify the validity on both methods.

Keywords

Robot collision detectionSafe human-robot interactionFuzzy modellingTime series modelling
Type
Articles
Information
Robotica , Volume 33 , Issue 9 , November 2015 , pp. 1886 - 1898
Copyright
Copyright © Cambridge University Press 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Wu, C.-J., “A modeling method for collision detection and motion planning of robots,” Robotica 11, 217–226 5 (1993).Google Scholar
2. Choi, J. S., Yoon, Y., Choi, M. H. and Lee, B. H., “Parameterized collision region for centralized motion planning of multiagents along specified paths,” Robotica 29, 1059–1073 (12 2011).Google Scholar
3. Flacco, F., Kroger, T., De Luca, A. and Khatib, O., “A Depth Space Approach to Human-Robot Collision Avoidance,” Proceedings of the 2012 IEEE International Conference on Robotics and Automation, IEEE (May 2012) pp. 338–345.Google Scholar
4. Lam, T. L., Yip, H. W., Qian, H. and Xu, Y., “Collision Avoidance of Industrial Robot Arms Using an Invisible Sensitive Skin,” Proceedings of the 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE (Oct. 2012) pp. 4542–4543.Google Scholar
5. Chen, W., Sun, Y. and Huang, Y., “A Collision Detection System for an Assistive Robotic,” In: Communications in Computer and Information Science (Li, K., Li, X., Ma, S. and Irwin, G. W., eds.) (Springer Berlin Heidelberg, 2010) pp. 117–123.Google Scholar
6. Yamada, Y., Hirasawa, Y., Huang, S., Umetani, Y. and Suita, K., “Human-robot contact in the safeguarding space,” IEEE/ASME Trans. Mechatronics 2 (4), 230–236 (1997).Google Scholar
7. Matsumoto, T. and Kosuge, K., “Collision Detection of Manipulator Based on Adaptive Control Law,” Proceedings of the 2001 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (Cat. No.01TH8556), volume 1. IEEE (2001) pp. 177–182.Google Scholar
8. Lu, S., Chung, J. H. and Velinsky, S. A., “Human-robot interaction detection: a wrist and base force/torque sensors approach,” Robotica 24 (04), 419 (Feb. 2006).Google Scholar
9. De Luca, A., Albu-Schaffer, A., Haddadin, S. and Hirzinger, G., “Collision Detection and Safe Reaction with the DLR-III Lightweight Manipulator Arm,” Proceedings of the 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems (Oct. 2006) pp. 1623–1630.Google Scholar
10. De Luca, A. and Mattone, R., “Sensorless Robot Collision Detection and Hybrid Force/Motion Control,” Proceedings of the 2005 IEEE International Conference on Robotics and Automation. IEEE (2005) pp. 999–1004.Google Scholar
11. Cho, C.-N., Kim, J.-H., Lee, S.-D. and Song, J.-B., “Collision detection and reaction on 7 DOF service robot arm using residual observer,” J. Mech. Sci. Technol. 26 (4), 1197–1203 (Apr. 2012).Google Scholar
12. Bouattour, M., Chadli, M., Chaabane, M. and Hajjaji, A., “Design of robust fault detection observer for Takagi-Sugeno models using the descriptor approach,” Int. J. Control Autom. Syst. 9 (5), 973–979 (Oct. 2011).Google Scholar
13. Sakellariou, J. S. and Fassois, S. D., “Vibration based fault detection and identification in an aircraft skeleton structure via a stochastic functional model based method,” Mech. Syst. Signal Process. 22 (3), 557–573 (2008).Google Scholar
14. Poulimenos, A. G. and Fassois, S. D., “Parametric time-domain methods for non-stationary random vibration modelling and analysis: A critical survey and comparison,” Mech. Syst. Signal Process. 20 (4), 763–816 (2006).Google Scholar
15. Guiarre, L., Bauso, D., Falugi, P. and Bamieh, B., “LPV model identification for gain scheduling control: An application to rotating stall and surge control problem,” Control Eng. Pract. 14, 351–361 (2006).Google Scholar
16. Chung, W., Fu, L.-C. and Hsu, S.-H., “Motion Control,” In: Springer Handbook of Robotics (Siciliano, B. and Khatib, O., eds.) (Berlin Heidelberg, 2008) pp. 133–159.Google Scholar
17. Passino, K. M. and Yurkovich, S., Fuzzy Control (Addison Wesley Publishing Company, California, 1997).Google Scholar
18. Fassois, S. D. and Kopsaftopoulos, F. P., “Statistical Time Series Methods for Vibration Based Structural Health Monitoring,” In: New Trends in Structural Health Monitoring (W. Ostachowicz and J. A. Güemes, eds.) (2013).Google Scholar
19. Ljung, L., System Identification: Theory for the User, 2nd edn. (Prentice Hall PTR, New Jersey, 1999).Google Scholar
20. International Standard. ISO 10182-1 INTERNATIONAL STANDARD. 2011 (2011).Google Scholar
21. Albu-Schäffer, A., Haddadin, S., Ott, Ch., Stemmer, A., Wimböck, T. and Hirzinger, G., “The DLR lightweight robot: design and control concepts for robots in human environments,” Ind. Robot. Int. J. 34 (5), 376–385 (Aug. 2007).Google Scholar