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60 J. Zeppenfeld and A. Herkersdorf far have shown that with a reasonable initial rule set, optimization generally occurs only for a short period following a change in workload, after which the system behavior enters a stable state. Finally, in addition to the autonomic elements connected to the CPU cores, further AEs are planned for the bus, memory and I/O modules. This will enable the parameterization of all system components, and provide additional global monitor signals to allow an even better optimization of the autonomic system. Acknowledgements We would like to thank our project partners Abdelmajid Bouajila and Walter Stechele from the Technische Universität München, Andreas Bernauer and Wolfgang Rosenstiel from the University of Tübingen, and Oliver Bringmann from the FZI in Karlsruhe. The ASoC project is sponsored by the DFG under SPP 1183 Organic Computing. References [1] Singhal, R.: Inside Intel Next Generation Nehalem Microarchitecture, http://blogs.intel.com/idf/2008/08/ sample_idf_sessions_inside_neh.php [2] Held, J., Bautista, J., Koehl, S.: From a Few Cores to Many: A Tera-scale Computing Research Overview. White Paper, http://www.intel.com [3] Henkel, J.: Closing the SoC design gap. Computer 36(9), 119–121 (2003) [4] Wang, Z.: Mapping Parallelism to Multi-cores: A Machine Learning Based Approach. In: 14th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, pp. 75–84 (2009) [5] Streichert, T., Strengert, C., Haubelt, C., Teich, J.: Dynamic Task Binding for Hardware/Software Reconfigurable Networks. In: SBCCI 2006, pp. 38–43 (2006) [6] Li, J., Ma, X., Singh, K., Schulz, M., de Supinski, B., McKee, S.: Machine Learning Based Online Performance Prediction for Runtime Parallelization and Task Scheduling. In: ISPASS, pp. 89–100 (2009) [7] Bernauer, A., et al.: An Architecture for Runtime Evaluation of SoC Reliability. In: Informatik für Menschen. Lecture Notes in Informatics, vol. P-93, pp. 177–185 (2006) [8] Zeppenfeld, J., Bouajila, A., Stechele, W., Herkersdorf, A.: Learning Classifier Tables for Autonomic Systems on Chip. Lecture Notes in Informatics, vol. 134, pp. 771–778. Springer, Gesellschaft für Informatik (2008) [9] Wilson, S.: Classifier fitness based on accuracy. Evolutionary Computation 3, 149–175 (1995)
Firefly Flashing Synchronization as Inspiration for Selfsynchronization of Walking Robot Gait Patterns Using a Decentralized Robot Control Architecture Bojan Jakimovski, Benjamin Meyer, and Erik Maehle Institute of Computer Engineering, University Lübeck, Germany bojan@iti.uni-luebeck.de, benjamin.meyer@informatik.uni-luebeck.de, maehle@iti.uni-luebeck.de www.iti.uni-luebeck.de Abstract. In this paper we introduce and elaborate a biologically inspired methodology for robot walking gait pattern self-synchronization using ORCA (Organic Robot Control Architecture). The firefly based pulse coupled biological oscillator concept has been successfully applied for achieving self-organized synchronization of walking robot gait patterns by dynamically prolonging and shortening of robot’s legs stance and swing phases. The results from the experiments done on our hexapod robot demonstrator show the practical usefulness of this biologically inspired approach for run-time self-synchronizing of walking robot gait pattern parameters. Keywords: self-synchronization, organic robot control architecture, dynamically prolongation and shortening of robot’s walking gait patterns, emergent robot gait synchronization, firefly synchronization, decentralized robot control architecture, robot gait pattern self-synchronization. 1 Introduction The increasing complexity of technical and robotic systems and complicated robotic systems modeling [1] [2] has introduced a need for development and applying new concepts and methodologies towards creating self-capable, more robust and dependable systems. To achieve this, engineers have used different biologically and organically inspired approaches [3][4]. For example for the domain of joint leg walking robots some of the algorithms for motor control and walking gait pattern generations have been inspired and developed on observations seen within animals [5] and functioning of the neural circuitry [6]. In that context research has been done on Central Pattern Generators (CPG) - related to nerve cells in animal’s spinal cord dedicated for generating periodic leg movement in animals and their application for robot control [7] [8] [9]. Additionally to this research various combinations have been also experimented on using artificial neural networks and genetic algorithms for robot gait pattern generation and gait control [10] [11] [12] [13]. However, just predefining some robot leg movements or generating gait patterns is sometimes not adequate for robust C. Müller-Schloer, W. Karl, and S. Yehia (Eds.): ARCS 2010, LNCS 5974, pp. 61–72, 2010. © Springer-Verlag Berlin Heidelberg 2010
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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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Abdullah, Tariq 174 Alima, Luc Onan
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