Cellular Automata in path planning navigation control applied in surveillance task using the e-Puck architecture

Abstract

Surveillance is a canonical task for robotics, so it is one of the most popular activities for a wide range of problems for robots. The problem's complexity increases proportionally with the number of rooms because the robot must act avoiding obstacles, saving time and energy. The proposed algorithms employ a combination of different search techniques to control a robot during a surveillance task. Our inspiration came from the possibility to use a discrete model employing a cellular automata combined with different heuristics approaches. This improvement of the best path selection was performed due to A* search optimization. Then, for solving collision problems a cellular automata rule is used in the expansion of obstacles, for a collision-free path. Finally, the surveillance task was implemented in a simulator called V-REP using e-Puck architecture. Simulation and experimental results indicate that this surveillance task could be implemented in low-cost architectures in a simplified way. The novelty consists of implementing these techniques in a realistic robot simulator, and also of a systematic analysis of each heuristic function used in contrast to the data structure optimization algorithm proposed herein.