Wind Aware Batch Informed Trees for Path Planning of Small UAS to Minimize Travel Time
Seung-Keol Ryu, MinJo Jung, Eric W. Frew, Michael Moncton, Han‐Lim Choi · 2025
Battery capacity limits the endurance of small unmanned aerial systems (UAS). This paper presents a wind-aware extension of the Batch Informed Tree (BIT*) planner that exploits both ambient horizontal winds and thermal updrafts to reduce travel time. A wind-informed heuristic guides heading and airspeed adjustments while ensuring collision-free paths in cluttered environments. Experiments were conducted in both a 2D urban environment and a 3D cluttered environment with thermal updrafts to validate the approach. In simulations over these scenarios, travel-time reductions range from about 3.5 % under mild winds to as much as 25 % when strong updrafts are available, for only a 1.5 % increase in path length. These results illustrate how even modest wind fields can be leveraged to extend mission duration over kilometer-scale flights. A notable drawback is the increased computation time. A planning runs up to 70–80 times slower than a simple distance-minimizing strategy. Future work will aim to accelerate the planner and relax the requirement for perfect wind-field knowledge, moving toward real-time applicability.