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A memetic algorithm approach for solving the task-based configuration optimization problem in serial modular and reconfigurable robots

Published online by Cambridge University Press:  08 December 2014

Saleh Tabandeh ,
William Melek ,
Mohammad Biglarbegian ,
Seong-hoon Peter Won  and
Chris Clark
Saleh Tabandeh
Affiliation:
Motion Control, Fanuc robotics, Rochester Hills, Michigan, USA
William Melek
Affiliation:
Mechanical and Mechatronics engineering, University of Waterloo, Waterloo, Ontario, Canada
Mohammad Biglarbegian
Affiliation:
School of Engineering, University of Guelph, Guelph, Ontario, Canada
Seong-hoon Peter Won*
Affiliation:
Mechanical and Mechatronics engineering, University of Waterloo, Waterloo, Ontario, Canada
Chris Clark
Affiliation:
Harvey Mudd College, Claremont, California, USA
*
*Corresponding author. E-mail: shwon@engmail.uwaterloo.ca
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Summary

This paper presents a novel configuration optimization method for multi degree-of-freedom modular reconfigurable robots (MRR) using a memetic algorithm (MA) that combines genetic algorithms (GAs) and a local search method. The proposed method generates multiple solutions to the inverse kinematics (IK) problem for any given spatial task and the MA chooses the most suitable configuration based on the search objectives. Since the dimension of each robotic link in this optimization is considered telescopic, the proposed method is able to find better solutions to the IK problem than GAs. The case study for a 3-DOF MRR shows that the MA finds solutions to the IK problem much faster than a GA with noticeably less reachability error. Additional case studies show that the proposed MA method can find multiple IK solutions in various scenarios and identify the fittest solution as a suboptimal configuration for the MRR.

Keywords

memetic algorithmsreconfigurable robotsconfiguration optimizationreachability
Type
Articles
Information
Robotica , Volume 34 , Issue 9 , September 2016 , pp. 1979 - 2008
Copyright
Copyright © Cambridge University Press 2014 

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References

1. Board on Manufacturing and National Research Council Engineering Design, Visionary Manufacturing Challenges for 2020 (National Academy Press, Washington, D.C., 1998).Google Scholar
2. Bi, Z. M. and Zhang, W. J., “Modularity technology in manufacturing: Taxonomy and issues,” Int. J. Adv. Manuf. Technol. 18, 381–390 (2001).CrossRefGoogle Scholar
3. Bi, Z. M., Lang, S. Y. T., Shen, W. and Wang, L., “Reconfigurable manufacturing systems: The state of the art,” Int. J. Prod. Res. 46 (4), 967–992 (2007).Google Scholar
4. Bi, Z. M., Lang, S. Y. T., Verner, M. and Orban, P., “Development of reconfigurable machines,” Int. J. Adv. Manuf. Technol. 39, 1227–1251 (2008).Google Scholar
5. Fukuda, T. and Nakagawa, S., “Dynamically Reconfigurable Robotic System,” Proceedings of the IEEE International Conference on Robotics and Automation, (1988) pp. 1581–1586.Google Scholar
6. Schmitz, D. E., Khosla, P. K. and Kanade, T., “The CMU Reconfigurable Modular Manipulator System,” Proceedings of the International Symposium and Exposition on Robots (designated 19th ISIR), Sydney, Australia (1988) pp. 473–488.Google Scholar
7. Paredis, C. J. J., Brown, H. B. and Khosla, P. K., “A rapidly deployable manipulator system,” Robot. Autom. Syst. 21, 289–304 (1997).Google Scholar
8. Chen, I.-M., “A Rapidly Reconfigurable Robotics Workcell and its Applications for T Engineering,” Innovation in Manufacturing Science and Technology Program, Singapore-MIT Alliance Annual Symposium, Traders Hotel, Singapore (2003).Google Scholar
9. Hui, R., Kircanski, N., Goldenberg, A., Zhou, C., Kuzan, P., Wiercienski, J., Gershon, D. and Sinha, P., “Design of the IRIS Facility - A Modular, Reconfigurable and Expandable Robot Test Bed,” Proceedings of the IEEE International Conference on Robotics and Automation, 3, (1993) pp. 155–160.Google Scholar
10. Matsumaru, T., “Design and Control of the Modular Robot System: TOMMS,” Proceedings of the IEEE International Conference on Robotics and Automation, (May 21–27, 1995) 2, pp. 2125–2131.Google Scholar
11. Li, Z., “Development and Control of a Modular and Reconfigurable Robot with Harmonic Drive Transmission System,” Master's Thesis, (Ontario, Waterloo: University of Waterloo, 2007).Google Scholar
12. Kirchhoff, S., “Intelligent Configuration Selection and Robust Control for Reconfigurable Robots,” Master's Thesis, (University of Waterloo and Technical Universitat Hamburg-Harburg, 2005).Google Scholar
13. Chen, I.-M. and Burdick, J. W., “Determining Task Optimal Modular Robot Assembly Configurations,” IEEE International Conference on Robotics and Automation, (1995) pp. 132–137.Google Scholar
14. Chen, I.-M., “On Optimal Configuration of Modular Reconfigurable Robots,” 4th International Conference on Control, Automation, Robotics and Vision, Singapore (1996) pp. 1855–1859.Google Scholar
15. Yang, G. and Chen, I.-M., “Reduced DOF Modular Robot Configurations,” 5th International Conference on Control, Automation, Robotics and Vision, Singapore (1998) pp. 1513–1517.Google Scholar
16. Chen, I.-M. and Yang, G., “An Evolutionary Algorithm for the Reduction of DOFs in Modular Reconfigurable Robots,” Proceeding of the ASME Dynamic System and Control Division, DSC-64, (1998) pp. 759–766.Google Scholar
17. Leger, C. and Bares, J., “Automated Synthesis and Optimization of Robot Configurations,” Proceedings of the 1998 ASME Design Engineering Technical Conferences, Atlanta, GA (1998).Google Scholar
18. Leger, C. and Bares, J., “Automated Task-Based Synthesis and Optimization of Field Robots,” Proceedings of the International Conference on Field and Service Robotics (FSR99), Pittsburgh, PA (1999).Google Scholar
19. Valsamos, H. and Aspragathos, N. A., “Design of a Versatile Passive Connector for Reconfigurable Robotic Manipulators with Articulated Anatomies and their Kinematic Analysis,” I*PROMS 2007 Virtual Conference, (2007).Google Scholar
20. Valsamos, H., Moulianitis, V. C. and Aspragathos, N. A., “Rapid Evaluation of Reconfigurable Robots Anatomies Using Computational Intelligence,” Proceedings of the 14th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part II, (Sep. 2010) pp. 341–350.CrossRefGoogle Scholar
21. Valsamos, C., Moulianitis, V. and Aspragathos, N., “Index based optimal anatomy of a metamorphic manipulator for a given task,” Int. J. Robot. Comput. Integr. Manuf. 28, 517–529 (2012).Google Scholar
22. Paredis, C. J. J. and Khosla, P. K., “An Approach for Mapping Kinematic Task Specifications into a Manipulator Design,” Proceedings of the 5th International Conference on Advanced Robotics, (ICAR '91), (1991) 1, pp. 556–561.Google Scholar
23. Paredis, C. J. J. and Khosla, P., “Kinematics design of serial link manipulators from task specifications,” Int. J. Robot. Res. 12 (3), 274–287 (1993).CrossRefGoogle Scholar
24. Kim, J.-O. and Khosla, P., “A Formulation for Task based Design of Robot Manipulators,” Proceedings of the 1993, IEEE/RSJ International Conference on Intelligent Robots and Systems, (1993) pp. 2310–2317.Google Scholar
25. Kim, J. and Khosla, P., “Design of Space Shuttle Tile Servicing Robot: An Application of Task based Kinematic Design,” IEEE International Conference on Robotics and Automation, (ICRA '93), (May 1993) 3, pp. 867–874.Google Scholar
26. Paredis, C. J. J. and Khosla, P. K., “Agent-Based Design of Fault Tolerant Manipulators for Satellite Docking,” Proceedings of the 1997, IEEE International Conference on Robotics and Automation, Albuquerque, New Mexico (Apr. 1997).Google Scholar
27. Li, Q. and Zhao, J., “A universal approach for configuration synthesis of reconfigurable robots based on fault tolerant indices,” Ind. Robot: Int. J. 39 (1), 69–78 (2012).Google Scholar
28. Han, J., Chung, W. K., Youm, Y. and Kim, S. H., “Task based Design of Modular Robot Manipulator using Efficient Genetic Algorithm,” Proceedings of the 1997, IEEE International Conference on Robotics and Automation, Albuquerque, New Mexico (Apr. 1997) pp. 507–512.Google Scholar
29. Gao, W., Wang, H., Jiang, Y. and Pan, X., “Task-based Configuration Synthesis for Modular Robot,” International Conference on Mechatronics and Automation (ICMA), (Aug. 2012) pp. 789–794.Google Scholar
30. Chocron, O., “Evolutionary design of modular robotic arms,” Robotica 26 (3), 323–330 (May 2008).Google Scholar
31. Dong, B. and Li, Y., “Multi-Objective-Based Configuration Generation and Optimization for Reconfigurable Modular Robot,” International Conference on Information Science and Technology (ICIST), (Mar. 26–28, 2011) pp. 1006–1010.Google Scholar
32. Ellery, A., An Introduction to Space Robotics (Springer Praxis publishing, UK, 2000).Google Scholar
33. Yang, G. and Chen, I., “Task-based optimization of modular robot configurations: Minimized degree-of-freedom approach,” Mech. Mach. Theory 35 (4), 517–540 (2000).Google Scholar
34. Moscato, P., “On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts: Towards Memetic Algorithms,” Technical Report Caltech Concurrent Computation Program 826, California Institute of Technology, USA (1989).Google Scholar
35. Dawkins, R., The Selfish Gene (Oxford University Press, Oxford, 1976).Google Scholar
36. Onwubolu, G. C. and Babu, B. V., New Optimization Techniques in Engineering (vol. 141 of Studies in Fuzziness and Soft Computing, Springer - Verlag, Berlin, Heidelberg, 2012).Google Scholar
37. Wolpert, D. and Macready, W., “No Free Lunch Theorems for Optimization,” IEEE Transactions in Evolutionary Computation, (1997) pp. 67–82.Google Scholar
38. Culberson, J., “On the futility of blind search: An algorithmic view of no free lunch,” Evol. Comput. 6 (2), 109–128 (1998).Google Scholar
39. Goldberg, D. and Voessner, S., “Optimizing Global-Local Search Hybrids,” Proceedings of the Genetic and Evolutionary Computation Conference, (1999) pp. 220–228.Google Scholar
40. Krasnogor, N. and Smith, J., “A tutorial for competent Memetic algorithms: Model, taxonomy, and design issues,” IEEE Trans. Evol. Comput. 9 (5), 474–488 (Oct. 2005).Google Scholar
41. Krasnogor, N., “Memetic Algorithms,” A Tutorial given in the 7th International Conference on Parallel Problem Solving from Nature (PPSN VII), (Sep. 2002).Google Scholar
42. Radcliffe, N. J. and Surry, P. D., “Formal Memetic Algorithms,” In: Selected Papers from AISB Workshop on Evolutionary Computing, (Springer-Verlag, London, UK, 1994) pp. 1–16.Google Scholar
43. Whitney, D. E., “Resolved motion rate control of manipulators and human prostheses,” IEEE Trans. Man-Mach. Syst. 10 (2), 47–53 (Jun. 1969).Google Scholar
44. Yoshikawa, T., “Manipulability of robotic mechanisms,” Int. J. Robot. Res. 4 (2), 3–9 (1985).Google Scholar
45. Maciejewski, A. A. and Klein, C. A., “Obstacle avoidance for kinematically redundant manipulators in dynamically varying environments,” Int. J. Robot. Res. 4, 109–117 (1985).Google Scholar
46. Wampler, C. W. II, “Manipulator inverse kinematic solutions based on vector formulations and damped least-squares methods,” IEEE Trans. Syst. Man Cybern. 16, 93–101, (1986).Google Scholar
47. Wolovich, W. A. and Elliot, H., “A Computational Technique for Inverse Kinematics,” Proceedings of the 23rd IEEE Conference on Decision and Control, (1984) pp. 1359–1363.Google Scholar
48. Baillieul, J., “Kinematic Programming Alternatives for Redundant Manipulators,” IEEE International Conference of Robotics and Automation, St. Louis (1985) pp. 722–728.Google Scholar
49. Seraji, H., “Configuration control of redundant manipulators: Theory and implementation,” IEEE J. Robot. Autom. 5 (4), 472–490 (1989).Google Scholar
50. Rao, C. R. and Mitra, S. K., Generalized Inverse of Matrices and its Applications (Wiley, New York, 1971).Google Scholar
51. Nakamura, Y. and Hanafusa, H., “Inverse kinematic solutions with singularity robustness for robot manipulator control,” Trans. ASME J. Dyn. Syst. Meas. Control 108, 163–171 (1986).Google Scholar
52. Zitzler, E., Deb, K. and Thiele, L., “Comparison of multiobjective evolutionary algorithms: Empirical results,” Evol. Comput. 8 (2), 173–195 (Feb. 1999).Google Scholar
53. Rudolph, G., “Evolutionary search under partially ordered fitness sets,” Proceedings of the International Symposium on Information Science Innovations in Engineering of Natural and Artificial Intelligent Systems, (1999) pp. 818–822.Google Scholar
54. Hart, W. E., Adaptive Global Optimization with Local Search Ph.D. Thesis (San Diego, California: University of California, 1994).Google Scholar
55. Miller, B. L. and Goldberg, D. E., “Genetic algorithms, tournament selection, and the effects of noise,” Complex Syst. 9, 193–212 (1995).Google Scholar
56. Deb, K. and Goyal, M., “A combined genetic adaptive search (GeneAS) for engineering design,” Comput. Sci. Inf. 26 (4), 30–45 (1996).Google Scholar
57. Tabandeh, S., Clark, C. M. and Melek, W. W., “Task-based Configuration Optimization of Modular and Reconfigurable Robots using a Multi-Solution Inverse Kinematics Solver,” Proceedings of the 2nd International Conference on Changeable, Agile, Reconfigurable and Virtual Production, Toronto, Canada (Jul. 2007) pp. 4–13.Google Scholar