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This study presents a new methodology for learning-based motion planning forautonomous exploration using aerial robots. Through the reinforcement learningmethod of learning through trial and error, the action policy is derived thatcan guide autonomous exploration of underground and tunnel environments. A newMarkov decision process state is designed to learn the robot's action policy byusing simulation only, and the results are applied to the real-worldenvironment without further learning. Reduce the need for the precision map ingrid-based path planner and achieve map-less navigation. The proposed methodcan have a path with less computing cost than the grid-based planner but hassimilar performance. The trained action policy is broadly evaluated in bothsimulation and field trials related to autonomous exploration of undergroundmines or indoor spaces.

Mapless Navigation: Learning UAVs Motion forExploration of Unknown Environments