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Robotic hand posture and compliant grasping control using operational space and integral sliding mode control

Published online by Cambridge University Press:  23 December 2014

Guido Herrmann ,
Jamaludin Jalani ,
Muhammad Nasiruddin Mahyuddin ,
Said G Khan  and
Chris Melhuish
Guido Herrmann*
Affiliation:
Bristol Robotics Laboratory, Department of Mechanical Engineering, Faculty of Engineering, University of Bristol, BS8 1TR, United Kingdom. E-mails: mexgh@bris.ac.uk
Jamaludin Jalani
Affiliation:
Department of Electrical Engineering Technology, Faculty of Engineering Technology, Universiti Tun Hussein Onn Malaysia, 86400, Parit Raja, Batu Pahat, Johor, Malaysia
Muhammad Nasiruddin Mahyuddin
Affiliation:
School of Electrical and Electronics Engineering, Universiti Sains Malaysia, Pulau Pinang, Malaysia
Said G Khan
Affiliation:
Department of Mechanical Engineering, College of Engineering Yanbu, Taibah University, Saudi Arabia
Chris Melhuish
Affiliation:
Bristol Robotics Laboratory, University of the West of England, Bristol, BS34 8QZ, United Kingdom
*
*Corresponding author. E-mail: g.herrmann@bris.ac.uk
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Summary

This paper establishes a novel approach of robotic hand posture and grasping control. For this purpose, the control uses the operational space approach. This permits the consideration of the shape of the object to be grasped. Thus, the control is split into a task control and a particular optimizing posture control. The task controller employs Cylindrical and Spherical coordinate systems due to their simplicity and geometric suitability. This is achieved by using an integral sliding mode controller (ISMC) as task controller. The ISMC allows us to introduce a model reference approach where a virtual mass-spring-damper system can be used to design a compliant trajectory tracking controller. The optimizing posture controller together with the task controller creates a simple approach to obtain pre-grasping/object approach hand postures. The experimental results show that target trajectories can be easily followed by the task control despite the presence of friction and stiction. When the object is grasped, the compliant control will automatically adjust to a specific compliance level due to an augmented compliance parameter adjustment algorithm. Once a specific compliance model has been achieved, the fixed compliance controller can be tested for a specific object grasp scenario. The experimental results prove that the Bristol Elumotion robot hand (BERUL) can automatically and successfully attain different compliance levels for a particular object via the ISMC.

Keywords

Robotic handGrasping postureCompliant controlPower graspingIntegral sliding mode control
Type
Articles
Information
Robotica , Volume 34 , Issue 10 , October 2016 , pp. 2163 - 2185
Copyright
Copyright © Cambridge University Press 2014 

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References

1. Jacobsen, S., Wood, J., Knutti, D. and Biggers, K., “The utah/m.i.t. dextrous hand: Work in progress,” Int. J. Robot. Res. 3 (4), 21–50 (1984).CrossRefGoogle Scholar
2. Shadow Robot Company, “Design of a Dextrous Hand for Advanced CLAWAR Applications,” Proceedings of CLAWAR (2003) pp. 691–698.Google Scholar
3. Grebenstein, M., Albu-Schaffer, A., Bahls, T., Chalon, M., Eiberger, O., Friedl, W., Gruber, R., Haddadin, S., Hagn, U., Haslinger, R., Hoppner, H., Jorg, S., Nickl, M., Nothhelfer, A., Petit, F., Reill, J., Seitz, N., Wimbock, T., Wolf, S., Wusthoff, T. and Hirzinger, G., “The DLR Hand Arm System,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (May 2011) pp. 3175–3182.CrossRefGoogle Scholar
4. Borst, C., Fischer, M., Haidacher, S., Liu, H. and Hirzinger, G., “DLR Hand II: Experiments and Experience with an Anthropomorphic Hand,” Proceedings of the IEEE International Conference on Robotics and Automation, ICRA '03, Vol. 1 (Sep. 2003) pp. 702–707.Google Scholar
5. Griffin, W. B., Findley, R. P., Turner, M. L. and Cutkosky, M. R., “Calibration and Mapping of a Human Hand for Dexterous Telemanipulation,” Proceedings of the ASME IMECE 2000 Symposium on Haptic Interfaces for Virtual Environments and Teleoperator Systems (2000) pp. 1–8.Google Scholar
6. Akin, D., Carignan, C. and Foster, A., “Development of a Four-Fingered Dexterous Robot end Effector for Space Operations,” Proceedings of the IEEE International Conference on Robotics and Automation, Vol. 3 (2002) pp. 2302–2308.Google Scholar
7. Geng, T., Lee, M. and Hlse, M., “Transferring Human Grasping Synergies to a Robot,” Mechatronics, 21 (1), 272–284 (2011).CrossRefGoogle Scholar
8. Wang, H., Low, K. H., Wang, M. Y. and Gong, F., “A Mapping Method for Telemanipulation of the non-Anthropomorphic Robotic Hands with Initial Experimental Validation,” Proceedings of the IEEE International Conference on Robotics and Automation, ICRA 2005, IEEE (2005) pp. 4218–4223.Google Scholar
9. Gioioso, G., Salvietti, G., Malvezzi, M. and Prattichizzo, D., “Mapping Synergies from Human to Robotic Hands with Dissimilar Kinematics: An Approach in the Object Domain,” IEEE Trans. Robot. 29 (4), 825–837 (2013).CrossRefGoogle Scholar
10. Bae, S. C., “Investigation of Hand Posture During Reach and Grasp for Ergonomic Applications,” Ph.D. Thesis (Ann Arbor, Michigan: University of Michigan, 2011).Google Scholar
11. Jalani, J., Mahyuddin, N., Herrmann, G. and Melhuish, C., “Active Robot hand Compliance using Operational Space and Integral Sliding Mode Control,” Proceedings of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM) (2013) pp. 1749–1754.Google Scholar
12. Jalani, J., Herrmann, G. and Melhuish, C., “Robust Active Compliance Control for Practical Grasping of a Cylindrical Object via a Multifingered Robot Hand,” Proceedings of the IEEE Conference on Robotics, Automation and Mechatronics (RAM) (2011) pp. 316–321.Google Scholar
13. DeSapio, V., Warren, J., Khatib, O. and Delp, S., “Simulating the task-level control of human motion: a methodology and framework for implementation.” Vis. Comput. 21 (5) 289–302 (2005).CrossRefGoogle Scholar
14. Siciliano, B., Sciavicco, L., Villani, L. and Oriolo, G., Robotics: Modelling, Planning and Control (Springer, London, 2009).CrossRefGoogle Scholar
15. Ham, R., Sugar, T., Vanderborght, B., Hollander, K. and Lefeber, D., “Compliant actuator designs,” IEEE Robot. Autom. Mag. 16 (3), 81–94 (Sep. 2009).CrossRefGoogle Scholar
16. Cutkosky, M. R., Robotic Grasping and Fine Manipulation (Kluwer Academic Publishers, Norwell, MA, USA, 1985).CrossRefGoogle Scholar
17. Johnson, K. L., Contact Mechanics (Cambridge University Press, Cambridge, UK, 1985).CrossRefGoogle Scholar
18. Shimoga, K. and Goldenberg, A., “Soft robotic fingertips,” Int. J. Robot. Res. 15 (4), 320–334 (1996).CrossRefGoogle Scholar
19. Biagiotti, L., Melchiorri, C., Tiezzi, P. and Vassura, G., “Modelling and Identification of Soft Pads for Robotic Hands,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2005) pp. 2786–2791.Google Scholar
20. Lionel Birglen, T. L. and Gosselin, C., Underactuated Robotics Hand (Springer-Verlag, Berlin, Heidelberg, 2008).CrossRefGoogle Scholar
21. BenAmor, H., Saxena, A., Hudson, N. and Peters, J., “Special issue on autonomous grasping and manipulation,” Auton. Robots 36 (1–2), 1–3 (2014).CrossRefGoogle Scholar
22. Liu, H. and Hirzinger, G., “Cartesian Impedance Control for the DLR Hand,” Proceedings of the Intelligent Robots and Systems, IROS'99, Vol. 1 (1999) pp. 106 –112.Google Scholar
23. Ott, C., Albu-Schaffer, A., Kugi, A. and Hirzinger, G., “On the passivity-based impedance control of flexible joint robots,” IEEE Trans. Robot. 24 (2), 416–429 (2008).CrossRefGoogle Scholar
24. Albu-Schaffer, A., Ott, C. and Hirzinger, G., “A unified passivity-based control framework for position, torque and impedance control of flexible joint robots,” Int. J. Robot. Res. 26 (1), 23–39 (2007).CrossRefGoogle Scholar
25. Khan, S., Herrmann, G., Pipe, A. and Melhuish, C., “Safe adaptive compliance control of a humanoid robotic arm with anti-windup compensation and posture control,” Int. J. Soc. Robot. 2 (3), 305–319 (2010).CrossRefGoogle Scholar
26. Chen, Z., Lii, N., Wimboeck, T., Fan, S., Jin, M., Borst, C. and Liu, H., “Experimental Study on Impedance Control for the Five-Finger Dexterous Robot Hand DLR-HIT II,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2010) pp. 5867–5874.Google Scholar
27. Mouri, K., Terashima, K., Minyong, P., Kitagawa, H. and Miyoshi, T., “Identification and Hybrid Impedance Control of Human Skin Muscle by Multi-Fingered Robot Hand,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, (IROS) (2007) pp. 2895–2900.Google Scholar
28. Xu, Y. and Paul, R., “On Position Compensation and Force Control Stability of a Robot with a Compliant Wrist,” Proceedings of the IEEE International Conference on Robotics and Automation, Vol. 2 (Apr. 1988) pp. 1173–1178.Google Scholar
29. Jaura, A., Osman, M. and Krouglicof, N., “Hybrid Compliance Control for Intelligent Sssembly in a Robot Work Cell,” Int. J. Prod. Res. 36 (9), 2573–2583 (1998).CrossRefGoogle Scholar
30. Kim, B.-H., Oh, S.-R., Suh, I. and Yi, G.-J., “A compliance control strategy for robot manipulators under unknown environment,” KSME Int. J. 14, 1081–1088 (2000).CrossRefGoogle Scholar
31. Khan, S., Herrmann, G., Pipe, T. and Melhuish, C., “Adaptive Multi-Dimensional Compliance Control of a Humanoid Robotic Arm with Anti-Windup Compensation,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2010) pp. 2218–2223.Google Scholar
32. Haddadin, S., Huber, F., Krieger, K., Weitschat, R., Albu-Schaffer, A., Wolf, S., Friedl, W., Grebenstein, M., Petit, F., Reinecke, J. and Lampariello, R., “Intrinsically Elastic Robots: The Key to Human like Performance,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2012) pp. 4270–4271.Google Scholar
33. Ott, C., Henze, B. and Lee, D., “Kinesthetic Teaching of Humanoid Motion based on Whole-Body Compliance Control with Interaction-Aware Balancing,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2013) pp. 4615–4621.Google Scholar
34. Sadeghian, H., Keshmiri, M., Villani, L. and Siciliano, B., “Null-Space Impedance Control with Disturbance Observer,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2012) pp. 2795–2800.Google Scholar
35. Zhang, T., Jiang, L., Fan, S., Wu, X. and Feng, W., “Development and experimental evaluation of multi-fingered robot hand with adaptive impedance control for unknown environment grasping,” Robotica (FirstView) 10, 1–18 (2014).Google Scholar
36. Zhang, T., Jiang, L., Wu, X., Feng, W., Zhou, D. and Liu, H., “Fingertip three-axis tactile sensor for multifingered grasping,” IEEE/ASME Trans. Mechatronics PP (99), 1–11 (2014).Google Scholar
37. Zhang, T., Liu, H., Jiang, L., Fan, S. and Yang, J., “Development of a flexible 3-d tactile sensor system for anthropomorphic artificial hand,” IEEE Sensors J., 13 (2), 510–518 (2013).CrossRefGoogle Scholar
38. Shi, J., Liu, H. and Bajcinca, N., “Robust control of robotic manipulators based on integral sliding mode,” Int. J. Control 81, 1537–1548 (2008).CrossRefGoogle Scholar
39. Jalani, J., Herrmann, G. and Melhuish, C., “Underactuated fingers controlled by robust and adaptive trajectory following methods,” Int. J. Syst. Sci. 45 (2), 120–132 (2014), DOI: 10.1080/00207721.2012.687866.CrossRefGoogle Scholar
40. Spiers, A., Herrmann, G. and Melhuish, C., “An Optimal Sliding Mode Controller Applied to Human Motion Synthesis with Robotic Implementation,” American Control Conference (ACC) (Jun. 30–Jul. 2 2010) pp. 991–996.CrossRefGoogle Scholar
41. Jalani, J., Herrmann, G. and Melhuish, C., “Robust Trajectory Following for Underactuated Robot Fingers,” Proceedings of the UKACC International Conference on CONTROL (Sep. 2010) pp. 495–500.CrossRefGoogle Scholar
42. Khatib, O., “A unified approach for motion and force control of robot manipulators: The operational space formulation,” IEEE J. Robot. Autom. 3 (1), 43–53 (1987).CrossRefGoogle Scholar
43. Khatib, O., “Inertial properties in robotic manipulation: An object-level framework,” Int. J. Robot. Res., 14 (1), 19–36 (1995).CrossRefGoogle Scholar
44. Yokoyama, M., Kim, G.-N. and Tsuchiya, M., “Integral sliding mode control with anti-windup compensation and its application to a power assist system,” J. Vib. Control 16 (4), 503–512 (2010).CrossRefGoogle Scholar
45. Defoort, M., Floquet, T., Kokosy, A. and Perruquetti, W., “Integral sliding mode control for trajectory tracking of a unicycle type mobile robot,” Integr. Comput.-Aided Eng. 13 (3), 277–288 (2006).CrossRefGoogle Scholar
46. Eker, I. and Akinal, S., “Sliding mode control with integral augmented sliding surface: Design and experimental application to an electromechanical system,” Electr. Eng., 90 (3), 189–197 (2008).CrossRefGoogle Scholar
47. Pressure Profile Systems, Inc. ConTacts C500 Single-Point Tactile Sensor (2014). http://www.pressureprofile.com.fqdns.net/UserFiles/File/PPS%20_ConTacts_Specsheets_08_18_10.pdf?PHPSESSID=8a2d3964082f7b93e2f3cfd0f8598cfd.Google Scholar