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70 B. Jakimovski, B. Meyer, and E. Maehle within its stance phase at time 20s. After this cycle, the next parameter change occurs by neighboring legs 0 and 4 in their stance phases at about 30s increasing from length parameter of 1 to length parameter of 2. In the next cycle the legs numbered 1 and 3 in their stance phases get synchronized to parameter length of 2 at about 35s. The synchronization continues within the leg number 2 which adjusts its parameter from length 1 to length 2 at 40s. In similar fashion the self-synchronization continues in the same experiment also for prolongation of the stance / swing phases from length parameter 2 to length parameter 3 for each of the legs. At time 95s all the legs are in synchronization having the stance / swing length parameter of 3. Similar to the prolongation, the shortening of stance / swing parameters in the same experiment takes further place and the length parameters by all the legs get shortened from length 3 to length 1 within their swing phases. With this last experiment we have demonstrated that also dynamic self-synchronization by increasing and decreasing of the gait parameters can be performed one after another during the walking of the robot. 6 Conclusion In this paper we have introduced a biologically inspired approach for gait patterns self-synchronization by joint leg walking robots. Throughout the paper we have described the relation to the firefly synchronization and by schematic representation of the self-synchronization by prolongation and shortening we have explained the principles of this concept. The results for experiments done on our hexapod robot OSCAR have proved that this approach can be practically applied for achieving a self-synchronization of the walking robot gait patterns using a decentralized robot control architecture. Such a concept can be also very useful for gait generation and speed adaptation for faulttolerant walking machines where legs sometimes get malfunctioned during the robot’s operation. Further work will include conducting more real case experiments in order to test the adaptability and robustness of the proposed methodology. Additional investigations will be also done on improving the information flow in such a decentralized robot control architecture. Acknowledgment. This work is partly supported by German Research Foundation - DFG (associated to SPP 1183, MA 1412/8-1). References 1. Han, B., Luo, Q., Wang, Q., Zhao, X.: A Research on Hexapod Walking Bio-robot’s Working Space and Flexibility. In: IEEE International Conference on Robotics and Biomimetics, pp. 813–817 (2006) 2. Cifuentes, N.J.R., Porras, J.H.G.: Modeling of legged robot based on Colombian insect observations. In: Electronics, Robotics and Automotive Mechanics Conference (CERMA), pp. 506–511 (2007)
Firefly Flashing Synchronization as Inspiration for Self-synchronization 71 3. German Science Foundation (DFG) Priority Program SPP 1183, Organic Computing (2004), http://www.organiccomputing.de/spp 4. Müller-Schloer, C.: Organic Computing – On the Feasibility of Controlled Emergence. In: Proceedings of the 2nd IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis, pp. 2–5 (2004) 5. Suzuki, H., Nishi, H., Aburadani, A., Inoue, S.: Animal Gait Generation for Quadrupedal Robot. Second International Conference on Innovative Computing, Information and Control (ICICIC), pp.20 (2007) 6. Delcomyn, F.: Neural basis for rhythmic behaviour in animals. Science 210, 492–498 (1980) 7. Barnes, D.: Hexapodal robot locomotion over uneven terrain. In: Proceedings of the 1998 IEEE International Conference on Control Application, vol. 1, pp. 441–445 (1998) 8. He, J., Lu, C., Yin, S.: The Design of CPG Control Module of the Bionic Mechanical Crab. In: Proceedings of the 2006 IEEE International Conference on Robotics and Biomimetics, Kunming, China (2006) 9. Billard, A., Ijspeert, A. J.: Biologically inspired neural controllers for motor control in a quadruped robot. In: IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN 2000), vol. 6, pp. 6637(2000) 10. Heinen, M.R., Osório, F.S.: Applying Neural Networks to Control Gait of Simulated Robots. In: 10th Brazilian Symposium on Neural Networks, pp. 39–44 (2008) 11. Manoonpong, P., Wörgötter, F.: Neural Control and Learning for Versatile, Adaptive, Autonomous Behavior of Walking Machines. In: International Conference on Advanced Computer Theory and Engineering, pp. 24–28 (2008) 12. Valsalam, V.K., Miikkulainen, R.: Modular Neuroevolution for Multilegged Locomotion. In: GECCO 2008, pp. 265–272 (2008) 13. Heinen, M.R., Osório, F.S.: Morphology and Gait Control Evolution of Legged Robots. In: IEEE Latin American Robotic Symposium, pp. 111–116 (2008) 14. Brockmann, W., Maehle, E., Mösch, F.: Organic Fault-Tolerant Control Architecture for Robotic Applications. In: 4th IARP/IEEE-RAS/EURON Workshop on Dependable Robots in Human Environments, Nagoya University, Japan (2005) 15. Serra, R., Zanarini, G.: Complex Systems and Cognitive Processes. Springer, New York (1990) 16. As, G.C., Odell, J.: Agents and Emergence. ACM, Distributed Computing (1998) 17. Mainzer, K.: Self-Organization and Emergence in Complex Dynamical Systems, pp. 590– 594. GI Jahrestagung (2004) 18. Randles, M., Lamb, D., Taleb-Bendiab, A.: Engineering Autonomic Systems Self- Organisation. In: Fifth IEEE Workshop on Engineering of Autonomic and Autonomous Systems, pp. 107–118 (2008) 19. Zheng, Z., Hu, G., Hu, B.: Phase Slips and Phase Synchronization of Coupled Oscillators. Physical Review Letters 81 (1998) 20. Buck, J.: Synchronous rhythmic flashing of fireflies, II. Quart. Rev. Biol. 63, 265–289 (1988) 21. Bottani, S.: Pulse-Coupled Relaxation Oscillators: From Biological Synchronization to Self-Organized Criticality. Phy. Rev. Lett. 74(21) 22. Case, J.F., Strause, L.G.: Neurally controlled luminescent systems. In: Herring, P.J. (ed.) Bioluminiscence in Action, pp. 331–366 (1978) 23. Buonomicini, M., Magni, F.: Nervous control flashing in the firefly Luciola italica L. Archives italiennes de Biologie, 323–338 (1967)
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Lecture Notes in Computer Science 5
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Volume Editors Christian Müller-Sc
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General Chair Organization Christia
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Organization IX Hartmut Schmeck Kar
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Keynote Table of Contents HyVM - Hy
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Table of Contents XIII JetBench:AnO
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How to Enhance a Superscalar Proces
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4 J. Mische et al. The Real-time Vi
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14 J. Mische et al. 16. Lickly, B.,
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16 G. Aşılıoğlu, E.M. Kaya, and
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18 G. Aşılıoğlu, E.M. Kaya, and
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120 P. Petoumenos et al. downsizing
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124 P. Petoumenos et al. comparable
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Exploiting Inactive Rename Slots fo
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128 M. Kayaalp et al. In a supersca
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130 M. Kayaalp et al. INSTRUCTION 1
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132 M. Kayaalp et al. time. Alterna
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134 M. Kayaalp et al. Fig. 3. Numbe
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Efficient Transaction Nesting in Ha
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140 Y. Liu et al. HTMs include TCC
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148 Y. Liu et al. decreases, partia
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Decentralized Energy-Management to
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152 B. Becker et al. Furthermore, t
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154 B. Becker et al. An optimizing
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156 B. Becker et al. smart-home man
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158 B. Becker et al. freedom like w
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160 B. Becker et al. Power [W] 4500
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EnergySaving Cluster Roll: Power Sa
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Effect of the Degree of Neighborhoo
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176 T. Abdullah et al. A zone based
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180 T. Abdullah et al. Messages 140
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184 T. Abdullah et al. % Matchmakin
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Abdullah, Tariq 174 Alima, Luc Onan
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