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International Journal of Automation and Computing 2018, Vol. 15 Issue (4) :431-442    DOI: 10.1007/s11633-016-1049-4
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Genetic Programming-based Self-reconfiguration Planning for Metamorphic Robot
Tarek Ababsa1, Noureddine Djedl1, Yves Duthen2
1 LESIA Laboratory, University of Biskra, PO Box 145 RP, Biskra 07000, Algerla;
2 IRIT Laboratory, University of Toulouse;
1 Capitole, 21 Alles de Brinne, Toulouse 31032, France
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Abstract This paper presents a genetic programming based reconfiguration planner for metamorphic modular robots. Initially used for evolving computer programs that can solve simple problems, genetic programming (GP) has been recently used to handle various kinds of problems in the area of complex systems. This paper details how genetic programming can be used as an automatic programming tool for handling reconfiguration-planning problem. To do so, the GP evolves sequences of basic operations which are required for transforming the robot s geometric structure from its initial configuration into the target one while the total number of modules and their connectedness are preserved. The proposed planner is intended for both Crystalline and TeleCube modules which are achieved by cubical compressible units. The target pattern of the modular robot is expressed in quantitative terms of morphogens diffused on the environment. Our work presents a solution for self reconfiguration problem with restricted and unrestricted free space available to the robot during reconfiguration. The planner outputs a near optimal explicit sequence of low-level actions that allows modules to move relative to each other in order to form the desired shape.
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KeywordsModular robots   unit-compressible modules   self-reconfiguration   genetic programming   reconfiguration planning     
Received: 2016-02-28; published: 2016-06-21
Corresponding Authors: Tarek Ababsa     Email: ababsatarek@yahoo.fr
About author: Tarek Ababsa received the B.Sc.and M.Sc.degrees in computer science from the University of Biskra,Algeria in 2004 and 2008,respectively. E-mail:ababsatarek@yahoo.fr;Noureddine Djedi received the B.Sc. degree in computer science from USTHB University,Algeria in 1986. E-mail:n.djedi@univ-biskra.dz;Yves Duthen received the Ph.D.degree from the University Paul Sabatier, France in 1983,and the French Habilitation degree in 1993 to become full professor.He is a research professor of artificial life and virtual reality at IRIT Lab,University of Toulouse 1-Capitole,France.He has worked in Image Synthesis during the 1980s and focused on behavioural simulation based on evolutionary mechanism since 1990.E-mail:yves.duthen@ut-capitole.fr
Cite this article:   
Tarek Ababsa, Noureddine Djedl, Yves Duthen. Genetic Programming-based Self-reconfiguration Planning for Metamorphic Robot[J]. International Journal of Automation and Computing , vol. 15, no. 4, pp. 431-442, 2018.
URL:  
http://www.ijac.net/EN/10.1007/s11633-016-1049-4      或     http://www.ijac.net/EN/Y2018/V15/I4/431
 
[1] J. Kubica, A. Casal, T. Hogg. Complex Behaviors from Local Rules in Modular Self-reconfigurable Robots, Proceedings of IEEE International Conference on Robotics and Automation (ICRA2001), vol. 1, pp. 360-3672001.
[2] M. Yim et al. Modular Self-Reconfigurable Robot Systems, Robotics and Automation Magazine, IEEE, vol. 14, pp. 43-52, 2007.
[3] B. Salemi, M. Moll, W.M. Shen. SUPERBOT:A Deployable, Multi-Functional, and Modular Self-Reconfigurable Robotic System, Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, Beijing, CHINA, pp. 3636-3641, 2006.
[4] D. Rus, M. Vona. Crystalline Robots:Self-Reconfiguration with Compressible Unit Modules, journal of Autonomous Robots, vol. 10, pp. 107-124, 2001.
[5] G. S. Chirikjian, A. Pamecha, I. Ebert-Uphoff. Evaluating efficiency of self reconfiguration in a class of modular robots, journal of Robotic Systems, vol. 13, pp. 317-338, 1996.
[6] A. Pamecha, G. Chirikjian. A useful metric for modular robot motion planning, Proceedings of IEEE International Conference on Robotics and Automation, vol. 1, pp. 442-447, 1996.
[7] A. Pamecha, I. Ebert-Uphoff, G. Chirikjian. Useful metrics for modular robot motion planning, IEEE Transaction on Robotics and Automation, vol. 13, pp. 531-545, 1997.
[8] G. Aloupis et al. Linear reconfiguration of cube-style modular robots, Computational Geometry-Theory and Applications, In:Tokuyama, T. (ed) ISAAC2007. LNCS, vol. 4835, pp. 208-219, 2007.
[9] T. Larkworthy, S. Ramamoorthy. An Efficient Algorithm for Self-Reconfiguration Planning in a Modular Robot, Proceedings of IEEE International Conference on Robotics and Automation (ICRA2010), pp. 5139-5146, 2010.
[10] Z. Butler and D. Rus. Distributed Planning and Control for Modular Robots with Unit-Compressible Modules, The International Journal of Robotics Research, vol. 22, pp. 699-715, 2003.
[11] J. Kubica, A. Casal, T. Hogg. Agent-based control for object manipulation with modular self-reconfigurable robots, Proceedings of the 17th International Joint Conference on Artificial Intelligence (IJCAI-2001), pp. 1344-1352, 2001.
[12] R. Kala. Multi-robot path planning using co-evolutionary genetic programming, Expert Systems with Applications 39, pp. 3817-3831, 2012.
[13] Y. Wang, L. Cheng. Optimal Formation of Multirobot Systems Based on a Recurrent Neural Network, IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 2, pp322-333, 2016. _
[14] D. Rus, M. Vona. A physical implementation of the self-reconfiguring crystalline robot, Proceedings of IEEE International Conference on Robotics and Automation (ICRA'00), vol. 2, pp. 1726-1733, 2000.
[15] J.R. Koza. Genetic programming:on the programming of computers by means of natural selection, MIT Press, 1992.
[16] J.R. Koza. Genetic Programming Ⅱ:Automatic Discovery of Reusable Programs, MIT Press, Cambridge Massachusetts, 1994.
[17] T.K. Paul, H. Iba. Genetic programming for classifying cancer data and controlling humanoid robots, Genetic Programming Theory and Practice IV, pp 41-59, 2007.
[18] T. Ababsa, N. Djedi, Y. Duthen, S.C. Blanc. Splittable Metamorphic Carrier Robots, Artificial Life 14, New York, MIT press, pp. 801-808, 2014.
[19] T. Steiner, J. Trommler, M. Brenn, Y. Jin, B. Sendhoff. Global shape with morphogen gradients and motile polarized cells, IEEE Congress on Evolutionary Computation (CEC'09), 2009.
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