Nonholonomic Robot Navigation of Mazes using Reinforcement Learning

Abstract

: Developing a navigation function for an unknown environment is a difficult task, made even more challenging when the environment has complex structure and the robot imposes nonholonomic constraints on the problem. Here we pose the problem of navigating an unknown environment as a reinforcement learning task for an Ackermann vehicle. We model environmental complexity using a standard characterization of mazes, and we show that training on complex maze architectures with loops (braid and partial braid mazes) results in an effective policy, but that for a more efficient policy, training on mazes without loops (perfect mazes) is to be preferred. Experimental results obtained in simulation are validated on a real robot operating both indoors and outdoors, assuming good localization and a 2D LIDAR to recover the local structure of the environment.