DC-RRT*: Dubins-Guided Curvature RRT* for 3D Path Planning of Unmanned Aerial Vehicles

Yekyung Jung, Beom-Seok Oh

Abstract: In this paper, we propose a Dubins-guided curvature rapidly exploring random tree* (DC-RRT*), a novel path planning method for unmanned aerial vehicles (UAVs). Different from conventional sampling-based path planning methods, DC-RRT* explicitly considers UAV-specific physical constraints, such as turning radius and vehicle size. Particularly, it employs a curvature-based node expansion strategy that explicitly accounts for both the UAV’s heading direction and minimum turning radius. The method enhances path convergence by attempting direct Dubins path connections between newly generated nodes and the target, while ensuring feasibility through comprehensive full-body collision checking. Our simulation study conducted on four obstacle scenarios, demonstrates that the proposed DC-RRT* generates paths with significantly fewer nodes than existing benchmarking methods, while maintaining computational efficiency.

External IDs:dblp:journals/access/JungO25