  This study presents a new methodology for learning-based motion planning for
autonomous exploration using aerial robots. Through the reinforcement learning
method of learning through trial and error, the action policy is derived that
can guide autonomous exploration of underground and tunnel environments. A new
Markov decision process state is designed to learn the robot's action policy by
using simulation only, and the results are applied to the real-world
environment without further learning. Reduce the need for the precision map in
grid-based path planner and achieve map-less navigation. The proposed method
can have a path with less computing cost than the grid-based planner but has
similar performance. The trained action policy is broadly evaluated in both
simulation and field trials related to autonomous exploration of underground
mines or indoor spaces.
