Cellular Automata Based Real-time Path-planning for Mobile Robots

Intelligent mobile robotic agents demand optimal motion planners with minimal query time. Most contemporary algorithms lack one of these two required aspects. This paper proposes an efficient path-planning scheme based on cellular automata (CA) that generates optimal paths in the minimum time. A Cellular automaton is evolved over the entire search space and subsequently used for the determination of the shortest path. This approach generates a parent-child relationship for each cell in order to minimize the search time. Performance comparisons with A*, Dijkstra's, D* and MPCNN have proven it to be time-efficient. Analysis, simulation and experimental results have proven it to be a robust and complete path-planning scheme. Also it has demonstrated to be time-efficient in both static and dynamic environments.