A line-graph path planner for performance constrained fixed-wing UAVs in wind fields

We present a runtime efficient approach to sampling-based path planning for fixed-wing unmanned aerial vehicles (UAV) based on line-graphs. Accounting for flight performance limits and the local prevailing wind, path planning is computationally expensive especially in 3D obstacle environments. A common approach is to solve the problem successively, i.e. to plan collision free paths, which are then transformed into feasible paths. However, this may compromise planning completeness if path smoothing fails. We show that line-graphs based on 3D probabilistic roadmaps can be used to effectively decouple the planning problem. The roadmap serves as a persistent free space representation of the environment and the corresponding line-graph is used to incorporate kinematic constraints to respect the fixed-wing flight performance limits in wind. Applying the A* graph-search on the line-graph instead of the 3D roadmap allows to efficiently find paths that respect these kinematic constraints without relying on path smoothing. Our results show that the presented approach can be used for near-realtime multi-query planning with varying wind conditions and flight performance constraints.