Robust Collision-free Lightweight Aerial Autonomy for Unknown Area Exploration

Collision-free path planning is an important requirement for autonomous exploration in unknown environments, especially when operating in confined spaces or near obstacles. This paper 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. Specifically, the generated path shows the consistent collision-free path in real-time by adopting the euclidean signed distance field-based grid search method. Simulations consistently show 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. All results indicate the high capability of the proposed flight autonomy framework for lightweight aerial-robot systems. Especially, our drone was able to perform an autonomous mission during our entry at the Tunnel Circuit competition (Phase 1) of the DARPA Subterranean Challenge.