Comparison of behavior-based and planning techniques on the small robot maze exploration problem

A comparison of behavior-based and planning approaches of robot control is presented in this paper. We focus on miniature mobile robotic agents with limited sensory abilities. Two reactive control mechanisms for an agent are considered—a radial basis function neural network trained by evolutionary algorithm and a traditional reinforcement learning algorithm over a finite agent state space. The control architecture based on localization and planning is compared to the former method.