  In narrow spaces, motion planning based on the traditional hierarchical
autonomous system could cause collisions due to mapping, localization, and
control noises, especially for car-like Ackermann-steering robots which suffer
from non-convex and non-holonomic kinematics. To tackle these problems, we
leverage deep reinforcement learning which is verified to be effective in
self-decision-making, to self-explore in narrow spaces without a given map and
destination while avoiding collisions. Specifically, based on our
Ackermann-steering rectangular-shaped ZebraT robot and its Gazebo simulator, we
propose the rectangular safety region to represent states and detect collisions
for rectangular-shaped robots, and a carefully crafted reward function for
reinforcement learning that does not require the waypoint guidance. For
validation, the robot was first trained in a simulated narrow track. Then, the
well-trained model was transferred to other simulation tracks and could
outperform other traditional methods including classical and learning methods.
Finally, the trained model is demonstrated in the real world with our ZebraT
robot.
