An Improved Cellular Automata-Based Model for Robot Path-Planning

摘要

Cellular automata (CA) are able to represent high complex phenomena and can be naturally simulated by digital processors due to its intrinsic discrete nature. CA have been recently considered for path planning in autonomous robotics. In this work we started by adapting a model proposed by Ioannidis et al. to deal with scenarios with a single robot, turning it in a more decentralized approach. However, by simulating this model we noticed a problem that prevents the robot to continue on its path and avoid obstacles. A new version of the model was then proposed to solve it. This new model uses CA transition rules with Moore neighborhood and four possible states per cell. Simulations and experiments involving real e-puck robots were performed to evaluate the model. The results show a real improvement in the robot performance.