Cellular automata machines: a new environment for modeling
Cellular automata machines: a new environment for modeling
Neural and automata networks: dynamical behavior and applications
Neural and automata networks: dynamical behavior and applications
Multilayered cellular automata
Theoretical Computer Science - Special issue: cellular automata
An algorithm for planning collision-free paths among polyhedral obstacles
Communications of the ACM
Artificial Life: A Report from the Frontier Where Computers Meet Biology
Artificial Life: A Report from the Frontier Where Computers Meet Biology
Robot Motion Planning
An Algorithm for Robot Path Planning with Cellular Automata
Proceedings of the Fourth International Conference on Cellular Automata for Research and Industry: Theoretical and Practical Issues on Cellular Automata
Real-time Path Planning for Navigation in Unknown Environment
TPCG '03 Proceedings of the Theory and Practice of Computer Graphics 2003
International Journal of Robotics Research
A Comprehensive Overview of the Applications of Artificial Life
Artificial Life
Cellular ants: A method to create collision free trajectories for a cooperative robot team
Robotics and Autonomous Systems
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Cellular automata (CA) model is a powerful instrument used in many applications. In this paper we present a reactive path-planning algorithm for a non-holonomic mobile robot on multilayered cellular automata. The robot considered has a preferential motion direction and has to move using smoothed trajectories, without stopping and turning in place, and with a minimum steering radius. We have implemented a new algorithm based on a directional (anisotropic) propagation of repulsive and attracting potential values in a multilayered cellular automata model. The algorithm finds all the optimal collision-free trajectories following the minimum valley of a potential hypersurface embedded in a 4D space, built respecting the imposed constraints. Our approach turns out to be distributed and incremental: whenever changing the initial or the final pose, or the obstacles distribution, the automata start evolving towards a new global steady state, looking for a new set of solutions. Because it reacts to obstacles distribution changes, it can be also used in unknown or dynamical environments in combination with a world modeler. The path-planning algorithm is applicable on a wide class of vehicles kinematics, selected changing a set of weights.