Intelligent visual servoing with extreme learning machine and fuzzy logic

Expert Systems with Applications

Volumn 72, Issue , 2017, Pages 344-356

  Yüksel, Tolga ^a  

^a BILECIK SEYH EDEBALI UNIVERSITY   (Turkey)

Author keywords

Extreme learning machine; Fuzzy logic; Image based visual servoing

Indexed keywords

COMPUTER CIRCUITS; COMPUTER VISION; DIFFERENTIAL EQUATIONS; FLEXIBLE MANIPULATORS; FUZZY LOGIC; INDUSTRIAL ROBOTS; INTELLIGENT ROBOTS; KNOWLEDGE ACQUISITION; LEARNING SYSTEMS; MANIPULATORS; MODULAR ROBOTS; PROBLEM SOLVING; ROBOT APPLICATIONS; VELOCITY;

EXTREME LEARNING MACHINE; IMAGE BASED VISUAL SERVOING; INITIAL VELOCITIES; INTELLIGENT METHOD; INTERACTION MATRICES; JOINT CONFIGURATION; PERFORMANCE METRICES; RESTRICTED REGIONS;

VISUAL SERVOING;

EID: 85007579218     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2016.10.048     Document Type: Article

References (37)

1. Armstrong, B., Khatib, O., Burdick, J., The explicit dynamic model and inertial parameters of the PUMA 560 arm. Proceedings. 1986 IEEE international conference on robotics and automation, Vol. 3, 1986, 510–518 http://doi.org/10.1109/ROBOT.1986.1087644.
2. Aström, K.J., Hagglund, T., Advanced PID control. 2006, ISA Publishing.
3. Chaumette, F., Potential problems of stability and convergence in image-based and position-based visual servoing. Lecture notes in control and information sciences, 1998, Springer-Verlag, 66–78.
4. Chaumette, F., Hutchinson, S., Visual servo control. I. Basic approaches. IEEE Robotics and Automation Magazine 13:4 (2006), 82–90 http://doi.org/10.1109/MRA.2006.250573.
5. Chaumette, F., Hutchinson, S., Visual servoing and visual tracking. Handbook of Robotics, 2008 http://doi.org/10.1007/978-3-540-30301-5_25.
6. Chesi, G., Hashimoto, K., Prattichizzo, D., Vicino, A., Keeping features in the field of view in eye-in-hand visual servoing: A switching approach. IEEE Transactions on Robotics 20:5 (2004), 908–913 http://doi.org/10.1109/TRO.2004.829456.
7. Chesi, G., Hung, Y.S., Global path-planning for constrained and optimal visual servoing. IEEE Transactions on Robotics 23:5 (2007), 1050–1060.
8. Collewet, C., Marchand, E., Chaumette, F., Visual servoing set free from image processing. Proceedings - IEEE international conference on robotics and automation, 2008, 81–86 http://doi.org/10.1109/ROBOT.2008.4543190.
9. Colley, S.J., Vector calculus. 2012, Pearson.
10. Corke, P.I., Robotics, vision & control: Fundamental algorithms in matlab. 2011, Springer.
11. Corke, P.I., Hutchinson, S.A., A new partitioned approach to image-based visual servo control. IEEE Transactions on Robotics and Automation 17:4 (2001), 507–515 http://doi.org/10.1109/70.954764.
12. Gans, N., Hutchinson, S., Corke, P.I., Performance tests for visual servo control systems, with application to partitioned approaches to visual servo control. International Journal of Robotics Research 22:10-11 (2003), 955–981 http://doi.org/10.1177/027836490302210011.
13. Gans, N.R., Hu, G., Nagarajan, K., Keeping multiple moving targets in the field of view of a mobile camera. IEEE Transactions on Robotics 27:4 (2011), 822–828.
14. Gonçalves, P.J., Mendonça, L.F., Sousa, J.M.C., Pinto, J.R.C., Uncalibrated eye-to-hand visual servoing using inverse fuzzy models. IEEE Transactions on Fuzzy Systems 16:2 (2008), 341–353.
15. Huang, G., What are extreme learning machines? Filling the gap between Frank Rosenblatt's dream and John von Neumann's puzzle. Cognitive Computation 7 (2015), 1–13 (April) http://doi.org/10.1007/s12559-015-9333-0.
16. Huang, G., Zhou, H., Ding, X., Zhang, R., Extreme learning machine for regression and multiclass classification. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics 42:2 (2012), 513–529.
17. Huang, G., Zhu, Q.-Y., Siew, C.-K., Extreme learning machine : Theory and applications. Neurocomputing 70 (2006), 489–501 http://doi.org/10.1016/j.neucom.2005.12.126.
18. Janabi-Sharifi, F., Deng, L., Wilson, W.J., Comparison of basic visual servoing methods. IEEE/ASME Transactions on Mechatronics 16:5 (2011), 967–983 http://doi.org/10.1109/TMECH.2010.2063710.
19. Jang, S.R., Sun, C.T., Neuro-fuzzy and soft computing: A computational approach to learning and machine intelligence. 1997, Prentice-Hall.
20. Kallem, V., Swensen, J.P., Hager, G.D., Cowan, N.J., Kernel-based visual servoing. IEEE/RSJ international conference on intelligent robots and systems, 2007, 1975–1980.
21. Kermorgant, O., Chaumette, F., Dealing with constraints in sensor-based robot control. IEEE Transactions on Robotics 30:1 (2014), 244–257.
22. Kosmopoulos, D.I., Robust Jacobian matrix estimation for image-based visual servoing. Robotics and Computer-Integrated Manufacturing 27:1 (2011), 82–87 http://doi.org/10.1016/j.rcim.2010.06.013.
23. Kumar, P.P., Behera, L., Visual servoing of redundant manipulator with Jacobian matrix estimation using self-organizing map. Robotics and Autonomous Systems 58:8 (2010), 978–990 http://doi.org/10.1016/j.robot.2010.04.001.
24. Lizarralde, F., Leite, A.C., Hsu, L., Costa, R.R., Adaptive visual servoing scheme free of image velocity measurement for uncertain robot manipulators. Automatica 49:5 (2013), 1304–1309 http://doi.org/10.1016/j.automatica.2013.01.047.
25. Malis, E., Chaumette, F., Boudet, S., 2 1/2 D visual servoing. IEEE Transactions on Robotics and Automation 15:2 (1999), 238–250 http://doi.org/10.1109/70.760345.
26. Mansard, N., Chaumette, F., Task sequencing for high-level sensor-based control. IEEE Transactions on Robotics 23:1 (2007), 60–72 http://doi.org/10.1109/TRO.2006.889487.
27. Mezouar, Y., Chaumette, F., Path planning for robust image-based control. IEEE Transactions on Robotics and Automation 18:4 (2002), 534–549.
28. Miljković, Z., Mitić, M., Lazarević, M., Babić, B., Neural network reinforcement learning for visual control of robot manipulators. Expert Systems with Applications 40 (2013), 1721–1736 http://doi.org/10.1016/j.eswa.2012.09.010.
29. Mohebbi, A., Keshmiri, M., Xie, W.F., A comparative study on eye-in-hand image-based visual servoing : Stereo vs. Mono. Journal of Integrated Design and Process Science 19:3 (2016), 25–54.
30. Nasisi, O., Carelli, R., Adaptive servo visual robot control. Robotics and Autonomous Systems 43 (2003), 51–78 http://doi.org/10.1016/S0921-8890(02)00370-6.
31. Schramm, F., Morel, G., Ensuring visibility in calibration-free path planning for image-based visual servoing. Robotics, IEEE Transactions on 22:4 (2006), 848–854 http://doi.org/10.1109/TRO.2006.878955.
32. Sebastian, J.M., Pari, L., Angel, L., Traslosheros, A., Uncalibrated visual servoing using the fundamental matrix. Robotics and Autonomous Systems 57:1 (2009), 1–10 http://doi.org/10.1016/j.robot.2008.04.002.
33. Tahri, O., Chaumette, F., Point-based and region-based image moments for visual servoing of planar objects. IEEE Transactions on Robotics 21:6 (2005), 1116–1127 http://doi.org/10.1109/TRO.2005.853500.
34. Wang, H., Jiang, M., Chen, W., Liu, Y., Visual servoing of robots with uncalibrated robot and camera parameters. Mechatronics 22:6 (2012), 661–668 http://doi.org/10.1016/j.mechatronics.2011.05.007.
35. Yoshikawa, T., Manipulability of robotic mechanisms. The International Journal of Robotics Research 4:2 (1985), 3–9.
36. Zhong, X., Zhong, X., Peng, X., Robust Kalman filtering cooperated Elman neural network Learning for vision-sensing-based robotic manipulation with global stability. Sensors 13 (2013), 13464–13486 http://doi.org/10.3390/s131013464.
37. Zhong, X., Zhong, X., Peng, X., Robots visual servo control with features constraint employing Kalman-neural-network filtering scheme. Neurocomputing 151 (2015), 268–277 http://doi.org/10.1016/j.neucom.2014.09.043.