  This study develops a robot mobility policy based on deep reinforcement
learning. Since traditional methods of conventional robotic navigation depend
on accurate map reproduction as well as require high-end sensors,
learning-based methods are positive trends, especially deep reinforcement
learning. The problem is modeled in the form of a Markov Decision Process (MDP)
with the agent being a mobile robot. Its state of view is obtained by the input
sensors such as laser findings or cameras and the purpose is navigating to the
goal without any collision. There have been many deep learning methods that
solve this problem. However, in order to bring robots to market, low-cost mass
production is also an issue that needs to be addressed. Therefore, this work
attempts to construct a pseudo laser findings system based on direct depth
matrix prediction from a single camera image while still retaining stable
performances. Experiment results show that they are directly comparable with
others using high-priced sensors.
