Improved BFS-based path planning algorithm with finite time generalized suboptimal search incorporating fixed-wing UAV flight constraints for complex low-altitude airspace

Abstract

The booming demands in low-altitude airspace impose stringent requirements on fixed-wing UAV path planning, emphasizing flyability, stealth, real-time performance, and high ground-following ratios. To achieve efficient and highly stealthy low-altitude variable-speed penetration in complex terrains, this study proposes two generalized suboptimal search algorithms — Generalized Suboptimal Search (GSS) and its focal-list enhanced variant (GSS-FS) — under the best-first search (BFS) framework. First, a dynamic node mechanism and constraint-aware neighbor expansion policy are designed to explicitly integrate fixed-wing UAVs’ flight constraints (e.g., attack angle, sideslip angle, angular rate). This addresses the “feasibility gap” in classical methods, where planned paths often fail to meet physical maneuverability requirements. Second, unlike traditional suboptimal algorithms with fragmented theoretical foundations (e.g., weighted A*, pwXD), GSS establishes a unified framework for generalized priority functions. This framework theoretically guarantees how suboptimal solutions approximate the optimal one, resolving the lack of systematic boundary estimation in existing approaches. Third, GSS-FS incorporates an optimized focal list and hybrid storage structure, achieving linear time complexity, which further improves its pathfinding efficiency on large-scale digital elevation maps (DEM). Simulations validate that the proposed algorithms can effectively search for suboptimal even optimal solutions that can weigh multiple flight indicators in finite time domain on large-scale DEM, making them suitable for high-dynamic low-altitude penetration missions.