  Collision-free path planning is an essential requirement for autonomous
exploration in unknown environments, especially when operating in confined
spaces or near obstacles. This study presents an autonomous exploration
technique using a small drone. A local end-point selection method is designed
using LiDAR range measurement and then generates the path from the current
position to the selected end-point. The generated path shows the consistent
collision-free path in real-time by adopting the Euclidean signed distance
field-based grid-search method. The simulation results consistently showed the
safety, and reliability of the proposed path-planning method. Real-world
experiments are conducted in three different mines, demonstrating successful
autonomous exploration flight in environments with various structural
conditions. The results showed the high capability of the proposed flight
autonomy framework for lightweight aerial-robot systems. Besides, our drone
performs an autonomous mission during our entry at the Tunnel Circuit
competition (Phase 1) of the DARPA Subterranean Challenge.
