  In this paper, we present a goal-driven autonomous mapping and exploration
system that combines reactive and planned robot navigation. First, a navigation
policy is learned through a deep reinforcement learning (DRL) framework in a
simulated environment. This policy guides an autonomous agent towards a goal
while avoiding obstacles. We develop a navigation system where this learned
policy is integrated into a motion planning stack as the local navigation layer
to move the robot towards the intermediate goals. A global path planner is used
to mitigate the local optimum problem and guide the robot towards the global
goal. Possible intermediate goal locations are extracted from the environment
and used as local goals according to the navigation system heuristics. The
fully autonomous navigation is performed without any prior knowledge while
mapping is performed as the robot moves through the environment. Experiments
show the capability of the system navigating in previously unknown surroundings
and arriving at the designated goal.
