For a mobile robot working in the real world, the ability to interpret information coming from the environment is crucial, both for its survival and for its accomplishing tasks. The cognitive capabilities of a robotic system are defined by the way in which the information gathered from sensors are processed to produce a specific action or behavior. Two broad classes can be distinguished: the cognitivist approach based on symbolic information processing and the emergent systems approach that is directed to the application of dynamical systems connected to the principles of self-organization [39]. Among the numerous solutions proposed by researchers, a great part is located in between the two main approaches.
In the recent past, research was directed towards endowing a robot with capabilities of self-creating an internal representation of the environment. Experiments in real world differ from applications in structured environments because they are mostly dynamically changing, so that it is impossible to program robot behaviors only on the basis of a priori knowledge. Moreover, the control loop must be able to process the different stimuli coming from the environment in a time that must be compatible with the real time applications. To solve these open problems, research activities have been focused on suitable solutions obtained taking inspiration from nature and applying biological principles to develop new control systems for perception—action purposes.
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References
Alba, L., Arena, P., De Fiore, S., Listan, J., Patané, L., Scordino, G., Webb, B.: Multi-sensory architectures for action-oriented perception. In: Proceedings of Microtechnologies for the New Millennium (SPIE'2007). Gran Canaria, Spain (2007)
Altera Corporation: Altera home page. http://www.altera.com.
Anafocus sl: Eye-ris producer home page. http://www.anafocus.com.
Aradi, I., Barna, G., Erdi, P.: Chaos and learning in the olfactory bulb. Int. J. Intell. Syst. 10(1), 89–117 (1995)
Arena, P., Crucitti, P., Fortuna, L., Frasca, M., Lombardo, D., Patané, L.: Turing patterns in rd-cnns for the emergence of perceptual states in roving robots. Bifurcation Chaos 17(1), 107–127 (2007)
Arena, P., De Fiore, S., Fortuna, L., Frasca, M., Patané, L., Vagliasindi, G.: Reactive navigation through multiscroll systems: from theory to real-time implementation. Autonomous Robots 25(1–2), 123–146 (2007)
Arena, P., Fortuna, L., Frasca, M., Hulub, M.: Implementation and synchronization of 3 × 3 grid scroll chaotic circuits with analog programmable devices. Chaos 16(1) (2006)
Arena, P., Fortuna, L., Frasca, M., Lo Turco, G., Patané, L., Russo, R.: A new simulation tool for actionoriented perception systems. In: Proceedings of 10th IEEE International Conference on Emerging Technologies and Factory Automation (EFTA'2005), pp. 19–22. Catania, Italy (2005)
Arena, P., Fortuna, L., Frasca, M., Lombardo, D., Patané, L.: Learning efference in cnns for perception-based navigation control. In: Proceedings of International Symposium on Nonlinear Theory and its Applications (NOLTA'2005), pp. 18–21. Bruges, Belgium (2005)
Arena, P., Fortuna, L., Frasca, M., Pasqualino, R., Patané, L.: Cnns and motor maps for bioinspired collision avoidance in roving robots. In: 8th IEEE Int. Workshop on Cellular Neural Networks and their Applications (CNNA'2004). Budapest (2004)
Arena, P., Fortuna, L., Frasca, M., Patané, L.: Sensory feedback in CNN-based central pattern generators. Int. J. Neural Syst. 13 (6), 349–362 (2003)
Arena, P., Fortuna, L., Frasca, M., Patané, L., Pavone, M.: Towards autonomous adaptive behavior in a bio-inspired cnn-controlled robot. In: Proceedings of IEEE International Symposium on Circuits and Systems (ISCAS'2006), pp. 21–24. Island of Kos, Grecia (2006)
Arena, P., Patané, L.: Spatial Temporal Patterns for Action Oriented Perception in Roving Robots. Springer, Berlin (2008)
Arkin, R.C.: Behavior-Based Robotics. MIT Press, Cambridge, MA (1998)
Boccaletti, S., Grebogi, C., Lai, Y.C., Mancini, H., Maza, D.: The control of chaos: theory and applications. Phys. Rep. 329, 103–197 (2000)
Freeman, W.J.: The physiology of perception. Sci. Am. 264(2), 78–85 (1991)
Freeman, W.J.: Characteristics of the synchronization of brain activity imposed by finite conduction velocities of axons. Bifurcation Chaos 10(10), 2307–2322 (1999)
Freeman, W.J.: A neurobiological theory of meaning in perception. Part I: Information and meaning in nonconvergent and nonlocal brain dynamincs. Bifurcation Chaos 13(9), 2493– 2511 (2003)
Freeman, W.J.: How and why brains create meaning from sensory information. Bifurcation Chaos 14(2), 515–530 (2004)
Freeman, W.J., Kozma, R.: Local-global interactions and the role of mesoscopic (intermediaterange) elements in brain dynamics. Behav. Brain Sci. 23(3), 401 (2000)
Gutierrez-Osuna, R., Gutierrez-Galvez, A.: Habituation in the kiii olfactory model with chemical sensor arrays. IEEE Trans. Neural Netw. 14(6), 1565–1568 (2003)
Harter, D., Kozma, R.: Chaotic neurodynamics for autonomous agents. IEEE Trans. Neural Netw. 16(3), 565–579 (2005)
Kohonen, T.: Self-organized formation of topologically correct feature maps. Bio. Cybern. 43(1), 59–69 (1972)
Kozma, R., Freeman, W.J.: Chaotic resonance — methods and applications for robust classification of noisy and variable patterns. Bifurcation Chaos 11(6), 1607–1629 (2000)
Kuniyoshi, Y., Sangawa, S.: Emergence and development of motor behavior from neural-body coupling. In: Proc. IEEE Int. Conference on Robotics and Automation (ICRA'2007). Rome, Italy (2007)
Lapidus, L., Seinfeld, J.: Numerical Solution of Ordinary Differential Equations. Academic Press, New York (1971)
Lu, J., Chen, G., Yu, X., Leung, H.: Design and analysis of multiscroll chaotic attractors from saturated function series. IEEE T Circuits-I 51(12), 2476–2490 (2004)
Makarov, V.A., Castellanos, N.P., Velarde, M.G.: Simple agents benefits only from simple brains. Trans. Eng. Comput. Tech. 15, 25–30 (2006)
Manganaro, G., Arena, P., Fortuna, L.: Cellular Neural Networks: Chaos, Complexity and VLSI processing. Springer, Berlin (1999)
Murray, J.D.: Mathematical Biology. Springer, Berlin (1993)
Ott, E., Grebogi, C., Yorke, J.A.: Controlling chaos. Phys. Rev. Lett. 64(11) (1990)
Pyragas, K.: Continuos control of chaos by self-controlling feedback. Phys. Lett. A 170, 421–428 (1992)
Pyragas, K.: Predictable chaos in slightly pertirbed unpredictable chaotic systems. Phys. Lett. A 181, 203–210 (1993)
Reeve, R., Webb, B.: New neural circuits for robot phonotaxis. Phil. Trans. R. Soc. A 361, 2245–2266 (2002)
Ritter, H., Martinetz, T., Schulten, K.: Neural Computation and Self-Organizing Maps. Addison Wesley, Reading, MA (1992)
Skarda, C.A., Freeman, W.J.: How brains make chaos in order to make sense of the world. Behav. Brain Sci. 10, 161–195 (1987)
SPARK Project: Spark eu project home page.http://www.spark.diees.unict.it
Steels, L., Brooks, R.A.: The Artificial Life Route to Artificial Intelligence: Building Embodied Situated Agents. Lawrence Erlbaum Associates, Hillsdale (1995)
Vernon, D., Metta, G., Sandini, G.: A survey of artificial cognitive systems: implications for the autonomous development of mental capabilities in computational agents. IEEE T. Evolut. Comput. 11(2), 151–180 (2007)
Webb, B., Scutt, T.: A simple latency dependent spiking neuron model of cricket phonotaxis. Biol. Cybern. 82 (3), 247–269 (2000)
Yalcin, M.E., Suykens, J.A.K., Vandewalle, J., Ozoguz, S.: Families of scroll grid attractors. Bifurcation Chaos 12(1), 23–41 (2002)
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Arena, P., De Fiore, S., Patané, L. (2009). Perception for Action in Roving Robots: A Dynamical System Approach. In: Adamatzky, A., Komosinski, M. (eds) Artificial Life Models in Hardware. Springer, London. https://doi.org/10.1007/978-1-84882-530-7_6
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