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A new motion planning method for discretely actuated hyper-redundant manipulators

Published online by Cambridge University Press:  27 February 2015

Alireza Motahari*
Department of Mechanical and Aerospace Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
Hassan Zohoor
Center of Excellence in Design, Robotics and Automation, Sharif University of Technology, &, The Academy of Sciences, Tehran, Iran
Moharam Habibnejad Korayem
Robotics Research Laboratory, Center of Excellence in Experimental Solid Mechanics and Dynamics, School of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran
*Corresponding author. E-mail:


A hyper-redundant manipulator is made by mounting the serial and/or parallel mechanisms on top of each other as modules. In discrete actuation, the actuation amounts are a limited number of certain values. It is not feasible to solve the kinematic analysis problems of discretely actuated hyper-redundant manipulators (DAHMs) by using the common methods, which are used for continuous actuated manipulators. In this paper, a new method is proposed to solve the trajectory tracking problem in a static prescribed obstacle field. To date, this problem has not been considered in the literature. The removing first collision (RFC) method, which is originally proposed for solving the inverse kinematic problems in the obstacle fields was modified and used to solve the motion planning problem. For verification, the numerical results of the proposed method were compared with the results of the genetic algorithm (GA) method. Furthermore, a novel DAHM designed and implemented by the authors is introduced.

Copyright © Cambridge University Press 2015 

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1. Chirikjian, G. S., “A Binary Paradigm for Robotic Manipulators,” Proceedings of the 1994, IEEE International Conference on Robotics and Automation, San Diego, (1994) pp. 3063–3070.Google Scholar
2. Sujan, V. A., Lichter, M. D. and Dubowsky, S., “Lightweight Hyper-redundant Binary Elements for Planetary Exploration Robots,” Proceedings of the 2001, IEEE/ASME International Conference on Advanced Intelligent Mechatronics, (2001) pp. 1273–1278.Google Scholar
3. Ebert-Uphoff, I., On the Development of Discretely-Actuated Hybrid-Serial-Parallel Manipulators Ph.D. Dissertation (Johns Hopkins University, 1997).Google Scholar
4. Suthakorn, J. and Chirikjian, G. S., “A new inverse kinematics algorithm for binary manipulators with many actuators,” Adv. Robot. 15 (2), 225244 (2001).CrossRefGoogle Scholar
5. Proulx, S. and Plante, J.-S., “Design and experimental assessment of an elastically averaged binary manipulator using pneumatic air muscles for magnetic resonance imaging guided prostate interventions,” Trans. ASME, J. Mech. Des. 133, 19 (2011).CrossRefGoogle Scholar
6. Chen, Q., Haddab, Y. and Lutz, P., “Microfabricated bistable module for digital microrobotics,” J. Micro-Nano Mech. 6, 112 (2011).CrossRefGoogle Scholar
7. Petit, L., Prelle, C., Dore, E. and Lamarque, F., “Digital Electromagnetic Actuators Array,” Proceedings of the 2010 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Montréal, Canada, (Jul. 6–9, 2010).Google Scholar
8. Chirikjian, G. S., “Inverse kinematics of binary manipulators using a continuum model,” J. Intell. Robot. Syst. 19, 522 (1997).CrossRefGoogle Scholar
9. Ebert-Uphoff, I. and Chirikjian, G. S., “Efficient workspace generation for binary manipulators with many actuators,” J. Robot. Syst. 12 (6), 383400 (1995).CrossRefGoogle Scholar
10. Chirikjian, G. S. and Ebert-Uphoff, I., “Numerical convolution on the Euclidean group with applications to workspace generation,” IEEE Trans. Robot. Automat. 14 (1), 123136 (1998).CrossRefGoogle Scholar
11. Motahari, A., Zohoor, H. and Korayem, M. H., “A new inverse kinematic algorithm for discretely actuated hyper-redundant manipulators,” Latin Am. Appl. Res. J. 43, 161168 (2013).Google Scholar
12. Motahari, A., Zohoor, H. and Korayem, M. H., “Discrete kinematic synthesis of discretely actuated hyper-redundant manipulators,” Robotica, 31 (7), 10731084 (2013).CrossRefGoogle Scholar
13. Motahari, A., Zohoor, H. and Korayem, M. H., “A new obstacle avoidance method for discretely actuated hyper-redundant manipulators,” Sci. Iranica, 19 (4), 10811091 (2012).CrossRefGoogle Scholar
14. Lanteigne, E. and Jnifene, A., “Obstacle avoidance of redundant manipulators using workspace density functions,” Trans. Can. Soc. Mech. Eng. 33 (4), 597608 (2009).Google Scholar
15. Khatib, O. Y., “Real-time obstacle avoidance for manipulators and mobile robots,” Int. J. Robot. Res. 5 (1), 9098 (1986).CrossRefGoogle Scholar
16. Agirrebietia, J., Avile's, R., de Bustos, I. F. and Ajuria, G., “A method for the study of position in highly redundant multibody systems in environments with obstacles,” IEEE Trans. Robot. Autom. 18 (2), 257262 (2002).CrossRefGoogle Scholar
17. Kavraki, L., Svestka, P., Latombe, J. C. and Overmars, M. H., “Probabilistic roadmaps for path planning in high-dimensional configuration spaces,” IEEE Trans. Robot. Autom. 12 (4), 566580 (1996).CrossRefGoogle Scholar
18. Sanchez, G. and Latombe, J. C., “A Single-Query Bi-directional Probabilistic Roadmap Planner with Lazy Collision Checking,” Int. Symposium on Robotics Research (ISRR), Lorne, Victoria, Australia, November (2001).Google Scholar
19. Kim, Y. Y., Jang, G. W. and Nam, S. J., “Inverse kinematics of binary manipulators by using the continuous-variable-base optimization method,” IEEE Trans. Robot. 22 (1), 3342 (2006).Google Scholar