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Evolutionary design of modular robotic arms

Published online by Cambridge University Press:  01 May 2008

O. Chocron
O. Chocron*
Affiliation:
Laboratorie de recherche en mécatronique, Ecole nationale d'ingénieurs de Brest, Technopôle Brest-Iroise, CS 73822 Brest Cedex 3, France
*
*Corresponding author. E-mail: [email protected]
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Summary

This paper proposes a method for task based design of modular serial robotic arms using evolutionary algorithms (EA). We introduce a 3D kinematics and a global optimization for both topology and configuration from task specifications. The search features revolute as well as prismatic joints and any number of DOF to build up a solution without using any design knowledge. A study of the evolution dynamics gives some keys to set evolution parameters that enable artificial evolution. An adapted algorithm dealing with the topology/configuration search tradeoff is proposed, descibed, and discussed. Illustrations of the algorithms results are given and conclusions are drawn from their analysis. Perspectives of this work are given, extending its reach to control and complex system design.

Keywords

Modular robotsTask-based designKinematic synthesisEvolutionary optimization
Type
Article
Information
Robotica , Volume 26 , Issue 3 , May 2008 , pp. 323 - 330
Copyright
Copyright © Cambridge University Press 2007

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References

1.Fukuda, T. and Nakagawa, S., “Dynamically reconfigurable robotic system,” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Philadelphia, PA, USA (1988) pp. 1581–1586.Google Scholar
2.Paredis, C. and Khosla, P., “A rapidly deployable manipulator system,” IEEE International Conference on Robotics and Automation (ICRA), Minneapolis, MN, USA (1996) pp. 1434–1439.Google Scholar
3.Yim, M., “A reconfigurable modular robot with many modes of locomotion,” Proceedings of International Conference on Advanced Mechatronics, Tokyo, Japan (1993) pp. 283–288.Google Scholar
4.Rus, D. and Chirikjian, G. S., “Self-reconfigurable robots,” Auton Rob 10 (1), 5–5 (2001).CrossRefGoogle Scholar
5.Ambrose, R. O., Design, construction and demonstration of modular, reconfigurable robots Ph.D. Thesis (Austin, USA, University of Texas, 1991).Google Scholar
6.Chen, I. M. and Burdick, J., “Determining task optimal modular robot assembly configurations,” IEEE International Conference on Robotics and Automation (ICRA), Nagoya, Japan (May 1995) pp. 132–137.Google Scholar
7.Hornby, S., Lipson, H. and Pollack, J. B., “Generative representations for the automated design of modular physical robots,” IEEE Trans. Robot. Automat., 19 (4), 703–719 (2003).CrossRefGoogle Scholar
8.Papadimitriou, C. H. and Steiglitz, K., Combinatorial Optimization- Algorithms and Complexity (Prentice-Hall, Englewoods Cliffs, NJ, USA, 1982).Google Scholar
9.Arora, J. S., Elwakeil, O. A. and Chahande, A. I., “Global optimization methods for engineering applications: a review.” J. Str. Opt. 9 (3-4), 137–159 (1995).CrossRefGoogle Scholar
10.Shen, S. N., Chew, M. and Issa, G. F., “Kinematic structural synthesis of mechanisms using knowledge-based systems,” J. Mech. Des. 117, 96–10 (1995)].Google Scholar
11.Kim, J. O. and Khosla, P., “A multi-population genetic algorithm and its application to design of manipulators,” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Raleigh, NC, USA (1992) pp. 279–286.Google Scholar
12.Paredis, C. and Khosla, P., Kinematic design of serial link manipulator from task specification. International Journal of Robotics Research, 12 (3), 274–287, (1993).CrossRefGoogle Scholar
13.Khwaja, A. A., Rahman, M. O. and Wagner, M. G., “Inverse kinematics of arbitrary robotic manipulators using genetic algorithms,” In: Advances in Robot Kinematics: Analysis and Control. (Lenarcic, J. and Husty, M. L., eds., (Kluwer Academic Publishers, 1998).Google Scholar
14.Yang, G. and Chen, I.-M.. Task-Based Optimization of Modular Robot Configurations—MDOF Approach. Mechanism and Machine Theory, 35 (4), 517–540 (2000).CrossRefGoogle Scholar
15.Chedmail, P. and Ramstein, E., “Robot mechanism synthesis and genetic algorithms,” IEEE International Conference on Robotics and Automation (ICRA), Minneapolis, MN, USA (April, 1996) pp. 3466–3471.Google Scholar
16.Pollack, J., Lipson, H., Ficici, S., Funes, P., Hornby, G. and Watson, R.. “Evolutionary techniques in physical robotics,” Proceedings of the Third International Conference on Evolvable Systems, Edinburgh, UK (April, 2000) pp. 175–186.CrossRefGoogle Scholar
17.Chocron, O. and Bidaud, Ph., “Genetic design of 3d modular manipulators,” IEEE International Conference on Robotics and Automation (ICRA), Albuquerque, NM, USA (April 1997) pp. 223–228.Google Scholar
18.Chocron, O. and Bidaud, Ph., “Evolutionnary algorithms in kinematic design of robotic systems,” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Grenoble, France (September 1997) pp. 1111–1117.Google Scholar
19.Chapelle, F., Chocron, O. and Bidaud, Ph., “Genetic programming for inverse kinematics problem approximation,” International Symposium on Robotics (ISR), Montreal, Canada (May 2000) pp. 5–11.Google Scholar
20.Sakka, S. and Chocron, O., “Optimal design, configurations and positions for a mobile manipulation task using genetic algorithms,” TENTH IEEE International Conference on Robot and Human Communication (ROMAN), Paris-Bordeaux, France (September 2001) pp. 268–273.Google Scholar
21.Yoshikawa, T.. Foundations of robotics: analysis and control (MIT Press Cambridge, MA, USA, 1990).Google Scholar
22.Goldberg, D. E., Genetic Algorithms in Search, Optimization and Machine Learning (Addison-Wesley Longman Publishing Co., Inc. Boston, MA, USA, 1989).Google Scholar
23.Bäck, T., Evolutionary Algorithms in Theory and Practise (Oxford University Press, New York, NY, USA 1996).CrossRefGoogle Scholar
24.Thornton, A. C., “Genetic algorithms versus simulated annealing: satisfaction of large sets of algebraic mechanical design constraints,” Proceedings of Artificial Intelligence in Design, Lausanne, Switzerland (August 1994) pp. 381–400.CrossRefGoogle Scholar
25.Yoshida, E., Murata, S., Tomita, K., Kurokawa, H. and Kokaji, S.. Distributed formation control for a modular mechanical system. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Grenoble, France (1997) pp. 1090–1097.Google Scholar
26.Yim, M., Shen, W.-M., Salemi, B., Rus, D., Moll, M., Lipson, H., Klavins, E., and Chirikjian, G. S., “Modular self-reconfigurable robot systems—challenges and opportunities for the fiture,” IEEE Robot. Automat. Mag. 14 (1), 43–52 (2007).CrossRefGoogle Scholar