Obtaining the Safety Margin Database for Fuzzy Control of the Grip Force of Robotic Hand in the Precision Grasp Task

Öz

The aim of this study is to collect data on human’s precise grip ability in order to adjust the parameters required to control the grip force of a robotic hand with a fuzzy logic controller. In the literature, the extra force that humans add on over of the minimum grip force when grasping and lifting objects is expressed as the safety margin. In this study, precision grip and lifting experiments were carried out for objects of different weights and different surface properties. In the experiments, the safety margin data that people apply while grasping and lifting objects of different weights and different surface properties in precision grasping were obtained. The obtained safety margin data will be evaluated as the database of the fuzzy logic controller to be designed. In this way, it will be ensured that a robotic hand can grip and lift an unknown object precisely. As a result of the experiments, safety margin rates varying between 9% and 20% were obtained depending on the varying object weight and coefficient of friction of surface. In this study, a fuzzy logic-based, variable safety margin-oriented grip force control approach is presented for robotic hand grip force control.

Anahtar Kelimeler

• Safety margin
• Robotic hand
• Grasping force
• Precision grasp

Hassas Kavrama Görevinde Robot Elin Kavrama Kuvvetinin Bulanık Kontrolü için Güvenlik Marjı Veri Tabanının Elde Edilmesi

Öz

Bu çalışmanın amacı, bir robot elin kavrama kuvvetinin kontrolünün bulanık mantık denetleyici ile yapılabilmesi için gerekli parametrelerin ayarlanması amacıyla insanların hassas kavrama yeteneğine ait verilerin toplanmasıdır. Literatürde, insanların nesneleri kavrayıp kaldırırken minimum kavrama kuvvetinin üzerine ekledikleri fazladan kuvvet, güvenlik marjı olarak ifade edilmektedir. Bu çalışmada insanlarla farklı ağırlıkta ve farklı yüzey özelliklerinde nesneler için hassas kavrama ve kaldırma deneyleri yapılmıştır. Yapılan deneylerde, insanların hassas kavrama görevinde farklı ağırlıkta ve yüzey özelliklerindeki nesneleri kavrayıp kaldırırken uyguladıkları güvenlik marjı verileri elde edilmiştir. Elde edilen güvenlik marjı verileri, tasarlanacak olan bulanık mantık denetleyicinin veri tabanı olarak değerlendirilecektir. Böylelikle bir robot elin, özellikleri bilinmeyen bir nesneyi hassas bir şekilde kavrayıp kaldırabilmesi sağlanacaktır. Yapılan deneyler sonucunda değişen nesne ağırlığına ve yüzey sürtünme katsayısına bağlı olarak %9 ile %20 arasında değişen güvenlik marjı oranları elde edilmiştir. Bu çalışma ile robot elin kavrama kuvveti kontrolü için bulanık mantık tabanlı, değişken güvenlik marjı odaklı bir kavrama kuvveti kontrol yaklaşımı ortaya konulmuştur.

Anahtar Kelimeler

• Güvenlik marjı
• Robot el
• Kavrama kuvveti
• Hassas kavrama

Kaynakça

1. Cutkosky, M. R. (1989). On grasp choice, grasp models, and the design of hands for manufacturing tasks. IEEE Transactions on robotics and automation, 5(3), 269-279.
2. Mavrakis, N., Ghalamzan, E. A. M., & Stolkin, R. (2017, September). Safe robotic grasping: Minimum impact-force grasp selection. In 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 4034-4041). IEEE.
3. Okamura, A. M., Smaby, N., & Cutkosky, M. R. (2000, April). An overview of dexterous manipulation. In Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No. 00CH37065) (Vol. 1, pp. 255-262). IEEE.
4. Bicchi, A., Salisbury, J. K., & Brock, D. L. (1993). Contact sensing from force measurements. The International Journal of Robotics Research, 12(3), 249-262.
5. Su, Z., Hausman, K., Chebotar, Y., Molchanov, A., Loeb, G. E., Sukhatme, G. S., & Schaal, S. (2015, November). Force estimation and slip detection/classification for grip control using a biomimetic tactile sensor. In 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids) (pp. 297-303). IEEE.
6. Pettersson-Gull, P., & Johansson, J. (2018). Intelligent robotic gripper with an adaptive grasp technique. Thesis for the Degree of Master of Science, Mälardalen University School of Innovation Design and Engineering, Västerås, Sweden.
7. Koda, Y., & Maeno, T. (2006, October). Grasping force control in master-slave system with partial slip sensor. In 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 4641-4646). IEEE.
8. Wettels, N., Parnandi, A. R., Moon, J. H., Loeb, G. E., & Sukhatme, G. S. (2009). Grip control using biomimetic tactile sensing systems. IEEE/ASME Transactions on Mechatronics, 14(6), 718-723.

9. Ho, V. A., & Hirai, S. (2011). Understanding Slip Perception of Soft Fingertips by Modeling and Simulating Stick-Slip Phenomenon. In Robotics: Science and Systems.
10. Morita, N., Nogami, H., Higurashi, E., & Sawada, R. (2018). Grasping force control for a robotic hand by slip detection using developed micro laser doppler velocimeter. Sensors, 18(2), 326.
11. Cutkosky, M. R., & Howe, R. D. (1990). Human grasp choice and robotic grasp analysis. In Dextrous robot hands (pp. 5-31). Springer, New York, NY.
12. Westling, G., & Johansson, R. S. (1984). Factors influencing the force control during precision grip. Experimental brain research, 53(2), 277-284.
13. Edin, B. B., Westling, G., & Johansson, R. S. (1992). Independent control of human finger‐tip forces at individual digits during precision lifting. The Journal of physiology, 450(1), 547-564.
14. Fu, Q., & Santello, M. (2018). Improving fine control of grasping force during hand–object interactions for a soft synergy-inspired myoelectric prosthetic hand. Frontiers in neurorobotics, 11, 71.
15. Kossowsky, H., Farajian, M., Milstein, A., & Nisky, I. (2020). The Effect of Between-Probe Variability in Haptic Feedback on Stiffness Perception and Grip Force Adjustment. bioRxiv.
16. Gaut, I. (2020). Evaluation of object attributes to study speed-accuracy trade-off of gloves using ISO 9241-411 standard. Thesis for the Degree of Master of Science, West Virginia University, Benjamin M. Statler College of Engineering and Mineral Resources, Morgantown, West Virginia.
17. Hadjiosif, A. M., & Smith, M. A. (2015). Flexible control of safety margins for action based on environmental variability. Journal of Neuroscience, 35(24), 9106-9121.
18. Tremblay, M. R., & Cutkosky, M. R. (1993, May). Estimating friction using incipient slip sensing during a manipulation task. In [1993] Proceedings IEEE International Conference on Robotics and Automation (pp. 429-434). IEEE.
19. Wettels, N., Parnandi, A. R., Moon, J. H., Loeb, G. E., & Sukhatme, G. S. (2009). Grip control using biomimetic tactile sensing systems. IEEE/ASME Transactions On Mechatronics, 14(6), 718-723.
20. Bergmann Tiest, W. M., & Kappers, A. M. (2019). The influence of visual and haptic material information on early grasping force. Royal Society open science, 6(3), 181563.
21. Johansson, R. S., & Flanagan, J. R. (2008). 6.05-Tactile sensory control of object manipulation in humans. Senses Compr. Ref. Acad. Press NY NY, 67-86.
22. Wiertlewski, M., Endo, S., Wing, A. M., & Hayward, V. (2013, April). Slip-induced vibration influences the grip reflex: A pilot study. In 2013 World Haptics Conference (WHC) (pp. 627-632). IEEE.
23. Gibo, T. L., Bastian, A. J., & Okamura, A. M. (2013). Grip force control during virtual object interaction: effect of force feedback, accuracy demands, and training. IEEE transactions on haptics, 7(1), 37-47.
24. Farajian, M., Leib, R., Kossowsky, H., Zaidenberg, T., Mussa-Ivaldi, F. A., & Nisky, I. (2020). Stretching the skin immediately enhances perceived stiffness and gradually enhances the predictive control of grip force. Elife, 9, e52653.
25. Grover, F. M. (2018). Intermittency between grip force and load force. (Doctoral dissertation, University of Cincinnati).
26. Wang, X., Xiao, Y., Zhao, Y., & Fan, X. (2017). Grasping force optimization algorithm of soft multi-fingered hand based on safety margin detection. jiqiren/Robot, 39, 844-852.
27. Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353.
28. Kocabaş, A. (2017). Design and optimization of a fuzzy logic based maximum power point tracker for pv panel (Doctoral dissertation, Karadeniz Teknik Üniversitesi).
29. Tüysüz, M. (2018). Hibrit güç sistemlerinde maksimum güç noktası takibi için bulanık denetleyicinin optimizasyonu (Doctoral dissertation, Karadeniz Teknik Üniversitesi).
30. Rodriguez, R. M., Martinez, L., & Herrera, F. (2011). Hesitant fuzzy linguistic term sets for decision making. IEEE Transactions on fuzzy systems, 20(1), 109-119.