Reactive navigation through multiscroll systems: from theory to real-time implementation

Abstract In this paper a new reactive layer for multi-sensory integration applied to robot navigation is proposed. The new robot navigation technique exploits the use of a chaotic system able to be controlled in real-time towards less complex orbits, like periodic orbits or equilibrium points, considered as perceptive orbits. These are subject to real-time modifications on the basis of environment changes acquired through a distributed sensory system. The strategy is inspired to the olfactory bulb neural activity observed in rabbits subject to external stimuli. The mathematical details of the approach are given including simulation results in a virtual environment. Furthermore the proposed strategy has been tested on an experimental environment consisting of an FPGA-based hardware driving an autonomous roving robot. The obtained results demonstrate the capability to perform a real-time navigation control.

[1]  Robert Kozma,et al.  Chaotic neurodynamics for autonomous agents , 2005, IEEE Transactions on Neural Networks.

[2]  Luigi Fortuna,et al.  A new simulation tool for action-oriented perception systems , 2005, 2005 IEEE Conference on Emerging Technologies and Factory Automation.

[3]  W. Freeman.  The physiology of perception. , 1991, Scientific American.

[4]  P. Arena,et al.  Cellular neural networks : chaos, complexity and VLSI processing , 1999 .

[5]  O. Khatib,et al.  Real-Time Obstacle Avoidance for Manipulators and Mobile Robots , 1985, Proceedings. 1985 IEEE International Conference on Robotics and Automation.

[6]  Kestutis Pyragas.  Continuous control of chaos by self-controlling feedback , 1992 .

[7]  P. Arena,et al.  Weak Chaos Control for Action-Oriented Perception: Real Time Implementation via FPGA , 2006, The First IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, 2006. BioRob 2006..

[8]  S. Boccaletti,et al.  The control of chaos: theory and applications , 2000 .

[9]  Helge J. Ritter,et al.  Neural computation and self-organizing maps - an introduction , 1992, Computation and neural systems series.

[10]  E. Süli,et al.  Numerical Solution of Ordinary Differential Equations , 2021, Foundations of Space Dynamics.

[11]  Walter J. Freeman,et al.  How and Why Brains Create Meaning from Sensory Information , 2004, Int. J. Bifurc. Chaos.

[12]  Paul F. M. J. Verschure,et al.  A real-world rational agent: unifying old and new AI , 2003 .

[13]  J. Fuster.  Cortex and mind : unifying cognition , 2003 .

[14]  Robert Kozma,et al.  Chaotic Resonance - Methods and Applications for Robust Classification of noisy and Variable Patterns , 2001, Int. J. Bifurc. Chaos.

[15]  Paul F. M. J. Verschure,et al.  Environmentally mediated synergy between perception and behaviour in mobile robots , 2003, Nature.

[16]  Kestutis Pyragas.  Predictable chaos in slightly perturbed unpredictable chaotic systems , 1993 .

[17]  Walter J. Freeman,et al.  A neurobiological theory of meaning in perception , 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..

[18]  Leon O. Chua,et al.  Linear and nonlinear circuits , 1987 .

[19]  Ronald C. Arkin,et al.  An Behavior-based Robotics , 1998 .

[20]  Paul F. M. J. Verschure,et al.  A real-world rational agent: unifying old and new AI , 2003, Cogn. Sci..

[21]  D. Harter,et al.  Evolving neurodynamic controllers for autonomous robots , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

[22]  P. Arena,et al.  Perceptive patterns for mobile robots via RD-CNN and reinforcement learning , 2005, 2005 9th International Workshop on Cellular Neural Networks and Their Applications.

[23]  W. Freeman,et al.  How brains make chaos in order to make sense of the world , 1987, Behavioral and Brain Sciences.

[24]  W. Freeman.  Simulation of chaotic EEG patterns with a dynamic model of the olfactory system , 1987, Biological Cybernetics.

[25]  Walter J. Freeman,et al.  Characteristics of the Synchronization of Brain Activity imposed by Finite conduction Velocities of axons , 2000, Int. J. Bifurc. Chaos.

[26]  Luigi Fortuna,et al.  Turing Patterns in RD-CNNS for the Emergence of Perceptual States in Roving Robots , 2007, Int. J. Bifurc. Chaos.

[27]  Xinghuo Yu,et al.  Design and analysis of multiscroll chaotic attractors from saturated function series , 2004, IEEE Transactions on Circuits and Systems I: Regular Papers.

[28]  Timothy W. McLain,et al.  Motion Planning using Potential Fields , 2003 .

[29]  L.O. Chua,et al.  Cellular neural networks , 1993, 1988., IEEE International Symposium on Circuits and Systems.