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Motion planning and posture control of multiple n-link doubly nonholonomic manipulators

Published online by Cambridge University Press:  05 March 2015

Bibhya Sharma*
Affiliation:
The University of the South Pacific, FIJI
Jito Vanualailai
Affiliation:
The University of the South Pacific, FIJI
Shonal Singh
Affiliation:
The University of the South Pacific, FIJI
*
*Corresponding author. E-mail: sharma_b@usp.ac.fj
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The paper considers the problem of motion planning and posture control of multiple n-link doubly nonholonomic mobile manipulators in an obstacle-cluttered and bounded workspace. The workspace is constrained with the existence of an arbitrary number of fixed obstacles (disks, rods and curves), artificial obstacles and moving obstacles. The coordination of multiple n-link doubly nonholonomic mobile manipulators subjected to such constraints becomes therefore a challenging navigational and steering problem that few papers have considered in the past. Our approach to developing the controllers, which are novel decentralized nonlinear acceleration controllers, is based on a Lyapunov control scheme that is not only intuitively understandable but also allows simple but rigorous development of the controllers. Via the scheme, we showed that the avoidance of all types of obstacles was possible, that the manipulators could reach a neighborhood of their goal and that their final orientation approximated the desired orientation. Computer simulations illustrate these results.

Type
Articles
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited
Copyright
Copyright © Cambridge University Press 2015

References

1. Burgard, W., Moors, M., Stachniss, C. and Schneider, F. E., “Coordinated multi-robot exploration,” IEEE Trans. Robot. 21 (3), 376386 (2005).Google Scholar
2. Fuller, J. L., Introduction, Programming, and Projects (Prentice Hall, 1998).Google Scholar
3. Hu, H., Tsui, P. W., Cragg, L. and Völker, N., “Architecture for multi-robot cooperation over the internet,” Int. J. Integr. Comput.-Aided Eng. 11 (3), 213226 (2004).Google Scholar
4. Sharma, B., Vanualailai, J. and Prasad, A., “Formation control of a swarm of mobile manipulators,” Rocky Mt. J. Math. 41 (3), 900940 (2011).Google Scholar
5. Sharma, B., New Directions in the Applications of the Lyapunov-based Control Scheme to the Findpath Problem Ph.D. Thesis (University of the South Pacific, Suva, Fiji Islands, July 2008). PhD Dissertation.Google Scholar
6. Yamaguchi, H., “A distributed motion coordination strategy for multiple nonholonomic mobile robots in cooperative hunting operations,” Robot. Auton. Syst. 43 (4), 257282 (2003).CrossRefGoogle Scholar
7. Sharma, B., Vanualailai, J. and Prasad, A., “Trajectory planning and posture control of multiple mobile manipulators,” Int. J. Appl. Math. Comput. 2 (1), 1131 (2010).Google Scholar
8. Campbell, A. and Annie, S. W., “Learning and Exploiting Knowledge in Multi-Agent Task Allocation Problems,” Proceedings of the Evolutionary Computation and Multi-Agent Systems and Simulation (ECoMASS) Workshop, London, England (July 7–11, 2007) pp. 2637–2642.Google Scholar
9. Seraji, H., “A unified approach to motion control of mobile manipulators,” Int. J. Robot. Res. 17 (2), 107118 (1998).Google Scholar
10. Tan, J. and Xi, N., “Unified Model Approach for Planning and Control for Mobile Manipulators,” Proceedings of the 2001 IEEE International Conference on Robotics and Automation, Seoul, Korea (May, 2001) pp. 3145–3152.Google Scholar
11. Mazur, A. and Arent, K., “Trajectory Tracking Control for Nonholonomic Mobile Manipulators,” In: Robot Motion and Control (Kozlouski, K., ed.) (Springer-Verlag, 2006) pp. 5571.CrossRefGoogle Scholar
12. Mazur, A. and Szakiel, D., “On path following control of nonholonomic mobile manipulators,” Int. J. Appl. Math. Comput. Sci. 19 (4), 561574 (2009).Google Scholar
13. Su, H. and Krovi, V., “Decentralized Dynamic Control of a Nonholonomic Mobile Manipulator Collective: A Simulation Study,” Proceedings of the 2008 ASME Dynamic Systems and Control Conference, Michigan, USA (Oct. 2008) pp. 1–8.Google Scholar
14. Tchoń, K., Jakubiak, J. and Zadarnowska, K., “Doubly Nonholonomic Mobile Manipulators,” Proceedings IEEE International Conference on Robotics and Automation, New Orleans (2004) pp. 4590–4595.Google Scholar
15. Chohra, A. D., Sif, F. and Talaoubrid, S., “Neural Navigation Approach of an Autonomous Mobile Robot in a Partially Structured Environment,” Proceedings of IAV'95, Finland (Jun. 1995) pp. 238–243.Google Scholar
16. Janglova, D., “Neural networks in mobile robot motion,” Int. J. Adv. Robot. Syst. 1 (1) 1522 (2004).Google Scholar
17. Skowronski, J. M., Nonlinear Lyapunov Dynamics (World Scientific Publishers, 1990).CrossRefGoogle Scholar
18. Vanualailai, J., Sharma, B. and Nakagiri, S., “An asymptotically stable collision-avoidance system,” Int. J. Non-Linear Mech. 43 (9) 925932 (2008).Google Scholar
19. Yang, S. X. and Meng, M., “An efficient neural network approach to dynamic robot motion planning,” Neural Netw. 13 (2), 143148 (2000).Google Scholar
20. Edelstein-Keshet, L., “Mathematical Models of Swarming and Social Aggregation,” Proceedings of 2001 International Symposium on Nonlinear Theory and its Applications, Miyagi, Japan (Oct.-Nov. 2001) pp. 1–7.Google Scholar
21. Ouarda, H., “Neural path planning for mobile robots,” Int. J. Syst. Appl. Eng. Dev. 5 (3), 367376 (1997).Google Scholar
22. Khatib, O., “Real time obstacle avoidance for manipulators and mobile robots,” Int. J. Robot. Res. 7 (1), 9098 (1986).Google Scholar
23. Lee, L-F. and Krovi, V., “A Standardized Testing-Ground for Artificial Potential-Field based Motion Planning for Robot Collectives,” Proceedings of the Performance Metrics for Intelligent Systems Workshop, Gaithersburg (Aug. 2006).Google Scholar
24. Nam, Y. S., Lee, B. H. and Ko, N. Y., “An Analytic Approach to Moving Obstacle Avoidance using Anartificial Potential Field,” Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 2 (Aug. 1995) pp. 482–487.Google Scholar
25. Song, P. and Kumar, V., “A Potential Field based Approach to Multi-Robot Manipulation,” Proceedings of the IEEE International Conference on Robotics & Automation, Washington, DC (May 2002).Google Scholar
26. Sharma, B., Vanualailai, J. and Chand, U., “Flocking of multi-agents in constrained environments,” Eur. J. Pure Appl. Math. 2 (3), 401425 (2009).Google Scholar
27. Sharma, B., Vanualailai, J. and Singh, S., “Tunnel passing maneuvers of prescribed formations,” Int. J. Robust Nonlinear Control 24 (5), 876901 (2014).Google Scholar
28. Sharma, B., Vanualailai, J. and Singh, S., “Lyapunov-based nonlinear controllers for obstacle avoidance with a planar n-link doubly nonholonomic manipulator,” Robot. Auton. Syst. 60, 14841497 (2012). http://dx.doi.org/10.1016/j.bbr.2011.03.031,.Google Scholar
29. Nakamura, Y., Chung, W. and Sørdalen, O. J., “Design and control of the nonholonomic manipulator,” IEEE Trans. Robot. Autom. 17 (1), 4859 (2003).Google Scholar
30. Pappas, G. J. and Kyriakopoulos, K. J., “Stabilization of non-holonomic vehicle under kinematic constraints,” Int. J. Control 61 (4), 933947 (1995).CrossRefGoogle Scholar
31. Brockett, R. W., “Asymptotic Stability and Feedback Stabilisation,” In: Differential Geometry Control Theory (Springer-Verlag, 1983) pp. 181191.Google Scholar
32. Khalil, H. K., Nonlinear Systems, 2nd ed. (Prentice-Hall, New Jersey, 1996).Google Scholar