Hexagon-based Q-learning for object search with multiple robots

Abstract

This paper presents the hexagon-based Q-leaning for object search with multiple robots. We organized an experimental environment with five small mobile robots, obstacles, and an object. Then we sent the robots to a hallway, where some obstacles were lying about, to search for a hidden object. In experiment, we used three control algorithms: a random search, an area-based action making (ABAM) process to determine the next action of the robots, and hexagon-based Q-learning to enhance the area-based action making process.