Hostname: page-component-cb9f654ff-p5m67 Total loading time: 0 Render date: 2025-08-29T06:01:08.122Z Has data issue: false hasContentIssue false
  • English
  • Français

Kinematic and dynamic analysis of a dexterous multi-fingered delta robot for object catching

Published online by Cambridge University Press:  03 February 2022

Sachin Kansal Open the ORCID record for Sachin Kansal [Opens in a new window]  and
Sudipto Mukherjee
Sachin Kansal*
Affiliation:
Computer Science Engineering Department, Thapar Institute of Engineering Technology, Patiala, Punjab, India
Sudipto Mukherjee
Affiliation:
Mechanical Engineering Department, Indian Institute of Technology Delhi, New Delhi, India
*
*Corresponding author. E-mail: sachin.kansal@thapar.edu
Get access Rights & Permissions [Opens in a new window]

Summary

This paper presents a new combined 9-DOF [3R-3R-3R] parallel architecture to perform the object catching in the real-time scenario. The architecture design is intended to carry heavy payload objects for the catching. This paper covers modelling the new architecture, kinematics, and dynamic analyses to compute torques/forces at the actuators. The simulation of kinematic and dynamic analysis in MATLAB. Vision sensors, encoders, PID controller, and current limiting are used to perform object catching. The architecture is the only delta robot application designed to catch regular-shaped objects in real-time scenarios.

Keywords

Delta robotcatchingRGB sensorRecurdynTMPID controllermulti-fingeredkinematicsdynamics

Information

Type
Reply
Information
Robotica , Volume 40 , Issue 8 , August 2022 , pp. 2878 - 2908
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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.)

Article purchase

Temporarily unavailable

References

Bonev, I.A., “Delta Robot — The Story of Success,” Parallel MIC, May 2001.Google Scholar
Zsombor-Murray, P. J., Descriptive Geometric Kinematic Analysis of Clavel’s Delta Robot (McGill University, Department of Mechanical. Engineering. Center for Intelligent Machine, Canada, 2004).Google Scholar
Tsai, L.-W., Position Analysis of Parallel Manipulator Robot Analysis – The Mechanics of Serial and Parallel Manipulators (John Willey & Sons, Inc., New York, 1999).Google Scholar
Alain, C., “Dynamic modeling of paral lel robots for computed-torque control implementation,” Int. J. Rob. Res. 17(12), 1325–1336 (1998).Google Scholar
Laribi, M. A., Romdhane, L. and Zeghlou, S. l, , “Analysis and dimensional synthesis of the DELTA robot for a prescribed workspace,” Mech. Mach. Theory 42(7), 859–870 (2006).CrossRefGoogle Scholar
Kosinska, A., Galicki, M. and Kedzior, K., Designing and Optimization of Parameters of Delta-4 Parallel Manipulator for a Given Workspace (Institute of Aeronautic and Applied Mechanics, Warsaw University of Technology, Poland, 2003).CrossRefGoogle Scholar
Tsai, L.-W., “Static Analysis of Parallel Manipulator,” In: Robot Analysis –The Mechanics of Serial and Parallel Manipulators (A Wiley-Interscience Publication, New York, NY, 1999) pp. 1–519.Google Scholar
Angeles, J. and Ma, O., “Dynamic simulation of -axis serial robotic manipulators using a natural orthogonal complement,” Int. J. Rob. Res. 7(10), 32–47 (1988).CrossRefGoogle Scholar
Angeles, J. and Lee, S., “The formulation of dynamical equations of holonomic mechanical system using a natural orthogonal complement,” ASME J. Appl. Mech. 55(5), 243–244 (1988).CrossRefGoogle Scholar
Saha, S.K., “Analytical expression for the inverted Inertia matrix of serial robots,” Int. J. Rob. Res. 18(1), 20–36 (1999).Google Scholar
Saha, S.K., “Dynamics of serial multibody system using the decoupled natural orthogonal complement matrices,” ASME J. Appl. Mech., 66, 986–996 (1999).CrossRefGoogle Scholar
Saha, S. K., “Recursive dynamic algorithms for serial, parallel, and closed-chain multibody systems,” Indo-US Workshop on Protein Kinematics & Protein Conformations (IISc, Bangalore, Dec. 10–11, 2007).Google Scholar
Saha, S.K., Introduction to Robotics (Tata McGraw-Hill, New Delhi, 2008).Google Scholar
Shinno, H., Yoshioka, H. and Sawano, H., “A newly developed long-range positioning table system with a sub-nanometer resolution,” CIRP Ann. Manuf. Technol. 60(1), 403–406 (2011).CrossRefGoogle Scholar
Atsumi, T., Nakamura, S., Furukawa, M., Naniwa, I. and Xu, J., “Triple stage-actuator system of head-positioning control in hard disk drives,” IEEE Trans. Magnets. 49(6), 2738–2743 (2013).CrossRefGoogle Scholar
Asada, H. and Slotine, J. J., Robot Analysis and Control (John Wiley & Sons, Inc., New York, NY, 1986).Google Scholar
Hogan, N. and Colgate, E., “Stability problem in contact tasks,” In: The Robotics Review (Oussama Khatib, John J. Craig, and Tomas Lozano, eds.) (The MIT Press, Cambridge, MA, 1989) pp.339–348.Google Scholar
McBean, J. and Breazeal, Cynthia, “Voice coil actuators for human-robot interaction,” International Conference on Intelligent Robots and Systems, Sendai (2004).Google Scholar
Philip, W. S., Nandha Kumar, N. and Ramadorai, A. K., “Vision-based manipulation of non-rigid object,” Proceeding of IEEE International Conference on Robotics and Automation,Minneapolis, MN (April 1996).Google Scholar
Yonemoto, S. and Taniguchi, R., “Vision-based 3D direct manipulation interface for smart interaction,” Proceedings of IEEE International Conference on Pattern Recognition, Quebec City, QC (2002) pp. 655–658.Google Scholar
Wei, Y., Goodwine, B., “Vision-based non-smooth kinematic stratified object manipulation,” Seventh International Conference on Control, Automation, Robotics and Vision (ICARCV02), Singapore (Dec 2002).Google Scholar
Park, C. H. and Ayanna, M. H., “Vision-based force guidance for improved human performance in a tele-operative manipulation system,” Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, San Diego, CA (2007).Google Scholar
Mkhitaryan, A. and Burschka, D., “Vision-based haptic multisensory for manipulation of soft, fragile object,” In: Sensors (Jin-Chern, ed.) (IEEE, Taipei, 2012) pp. 1–4.Google Scholar
Chen, S. Y., Zhang, J., Zhang, H. and Kwok, N. M., “Intelligent lighting control for vision-based robotic manipulation,” IEEE Trans. Ind. Electron. 59, 3254–3263 (2012).CrossRefGoogle Scholar
Sachin, K. and Sudipto, M., “Visi on-Based Kinematics Analysis of the Delta Robot for object Catching,” Robotica 1(31), 2661–2678 (2021).Google Scholar
Sachin, K., Rajesh, K. and Sudipto, M., “Color Invariant State Estimator to predict the object trajectory and catch Dexterous Multi-fingered Delta Robot,” In: Multimedia Tools and Applications, 2020.Google Scholar
Sachin, K. and Sudipto, M., “Automa tic single view monocular camera calibration based object manipulation using novel dexterous multi-fingered delta robot,” Neural Comput. Appl. 7(31), 2661–2678 (2017).Google Scholar
John, C., Introduction to Robotics: Mechanics and Control, 4th Edition.Google Scholar
Gupta, V., Chaudhary, H. and Saha, S., “Dyna mics and actuating torque optimization of planar robots,” J. Mech. Sci. Technol. 1, 2699–2704 (2015).CrossRefGoogle Scholar
Kushal, A. M., Bansal, V. and Banerjee, S., “A simple method for interactive 3D reconstruction and camera calibration from a single view,” Proceedings: Indian Conference in Computer Vision, Graphics and Image Processing, Mumbai (2002).Google Scholar
Criminisi, A., Reid, I. and Zisserman, A., “Single view metrology,” Int. J. Comput. Vision 40(2), 123–148 (2000).CrossRefGoogle Scholar
Criminisi, A., “Single- view metrology: Algorithms and applications,” Invited Paper (Microsoft Research, One Microsoft Way, Redmond, WA, 2002).CrossRefGoogle Scholar
Wang, G., Tsui, H., Zhanyi, H. and Wu, F., “Camera calibration and 3D reconstruction from a single view based on scene constraints,” ELSEVIER, Image Vision Comput. 23, 311–323 (2005).CrossRefGoogle Scholar