Waves in isotropic Totalistic Cellular Automata: Application to Real-Time robot Navigation

Totalistic cellular automata (CA) are an efficient tool for simulating numerous wave phenomena in discrete media. However, their inherent anisotropy often leads to a significant deviation of the model results from experimental data. Here, we propose a computationally efficient isotropic CA with the standard Moore neighborhood. Our model exploits a single postulate: the information transfer in an isotropic medium occurs at constant rate. To fulfill this requirement, we introduce in each cell a local counter keeping track of the distance run by the wave from its source. This allows maintaining the wave velocity constant in all possible directions even in the presence of nonconductive local areas (obstacles) with complex spatial geometry. Then, we illustrate the model on the problem of real-time building of cognitive maps used for navigation of a mobile robot. The isotropic property of the CA helps obtaining “smooth” trajectories and hence natural robot movement. The accuracy and flexibility of the approach are proved experimentally by driving the robot to a target avoiding collisions with obstacles.

[1]  Ioannis G. Karafyllidis,et al.  Simulation of two-dimensional photoresist etching process in integrated circuit fabrication using cellular automata , 1995 .

[2]  Gerardo M. Ortigoza Unstructured triangular cellular automata for modeling geographic spread , 2015, Appl. Math. Comput..

[3]  Harish Kundra,et al.  A Review paper of Navigation and Pathfinding using Mobile Cellular Automata , 2014 .

[4]  Modelling the morphology of migrating bacterial colonies , 2010 .

[5]  Costin-Radu Boldea,et al.  A Particle Cellular Automata Model for Fluid Simulations , 2009 .

[6]  Illah R. Nourbakhsh,et al.  A survey of socially interactive robots , 2003, Robotics Auton. Syst..

[7]  Thierry Hoinville,et al.  A hexapod walker using a heterarchical architecture for action selection , 2013, Front. Comput. Neurosci..

[8]  Valeri A. Makarov,et al.  Prediction-for-CompAction: navigation in social environments using generalized cognitive maps , 2015, Biological Cybernetics.

[9]  Qing‐An Huang,et al.  A modified cellular automata algorithm for the simulation of boundary advancement in deposition topography simulation , 2005 .

[10]  Rodrigo Weber dos Santos,et al.  3D Heart Modeling with Cellular Automata, Mass-Spring System and CUDA , 2013, PaCT.

[11]  Emilio Kropff,et al.  Place cells, grid cells, and the brain's spatial representation system. , 2008, Annual review of neuroscience.

[12]  Valeri A. Makarov,et al.  Compact internal representation of dynamic situations: neural network implementing the causality principle , 2010, Biological Cybernetics.

[13]  M. Marek Grid anisotropy reduction for simulation of growth processes with cellular automaton , 2013 .

[14]  Sepideh Adabi,et al.  A Cellular Automata Based Algorithm for Path Planning in Multi-Agent Systems with A Common Goal , 2008 .

[15]  Antonios Gasteratos,et al.  Efficient Robot Path Planning in the Presence of Dynamically Expanding Obstacles , 2012, ACRI.

[16]  Georgios Ch. Sirakoulis,et al.  A cellular automaton for the propagation of circular fronts and its applications , 2005, Eng. Appl. Artif. Intell..

[17]  T. Niizato,et al.  An adaptive and robust biological network based on the vacant-particle transportation model. , 2011, Journal of theoretical biology.

[18]  Mio Kobayashi Isotropic Cellular Automaton for Excitable Media with Random Neighbor Selection , 2014, ACRI.

[19]  D. H. Anderson,et al.  Graphical simulation of bushfire spread , 1990 .

[20]  Laure Tougne,et al.  Discrete Parabolas and Circles on 2D Cellular Automata , 1999, Theor. Comput. Sci..

[21]  Georgios Ch. Sirakoulis,et al.  An FPGA processor for modelling wildfire spreading , 2013, Math. Comput. Model..

[22]  P. Petrov Modeling and adaptive path control of a differential drive mobile robot , 2010 .

[23]  Tauseef Gulrez,et al.  CELLULAR AUTOMATA BASED PATH-PLANNING ALGORITHM FOR AUTONOMOUS MOBILE ROBOTS , 2005 .

[24]  Ioannis G. Karafyllidis,et al.  A model for predicting forest fire spreading using cellular automata , 1997 .

[25]  Valeri A. Makarov,et al.  Neural Network Architecture for Cognitive Navigation in Dynamic Environments , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[26]  B. Hess,et al.  Isotropic cellular automaton for modelling excitable media , 1990, Nature.

[27]  M. Rettenmayr,et al.  Perspectives for cellular automata for the simulation of dendritic solidification – A review , 2014 .

[28]  J. O'Keefe,et al.  The hippocampus as a spatial map. Preliminary evidence from unit activity in the freely-moving rat. , 1971, Brain research.

[29]  David E. Irwin,et al.  Modern mental chronometry , 1988, Biological Psychology.

[30]  Faraz Kunwar,et al.  Cellular Automata Based Real-Time Path-Planning for Mobile Robots , 2012, ICARCV 2012.

[31]  Zhang Yi,et al.  Real-Time Robot Path Planning Based on a Modified Pulse-Coupled Neural Network Model , 2009, IEEE Transactions on Neural Networks.

[32]  M. Castro,et al.  An Algorithm for Robot Path Planning with Cellular Automata , 2000, ACRI.

[33]  Valeri A. Makarov,et al.  FPGA implementation of a modified FitzHugh-Nagumo neuron based causal neural network for compact internal representation of dynamic environments , 2011, Microtechnologies.

[34]  Tomás Lozano-Pérez,et al.  An algorithm for planning collision-free paths among polyhedral obstacles , 1979, CACM.