Wind Aware Batch Informed Trees for Path Planning of Small UAS to Minimize Travel Time

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.

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Published
2025-07-16
DOI
https://doi.org/10.2514/6.2025-3687
Primary Topic
Robotic Path Planning Algorithms
Type
article
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article

Wind Aware Batch Informed Trees for Path Planning of Small UAS to Minimize Travel Time

Seung-Keol Ryu, Eric W. Frew, Michael Moncton, Han‐Lim Choi et al.
Robotic Path Planning Algorithms
article

Wind Aware Batch Informed Trees for Path Planning of Small UAS to Minimize Travel Time

Seung-Keol Ryu, Eric W. Frew, Michael Moncton, Han‐Lim Choi, Myung‐Jin Jung
article en

Abstract

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.

Korea Advanced Institute of Science and Technology (KR), University of Colorado Boulder (US), University of Colorado System (US)
Climate action
Openalex Percentile: Top 84%
Robotic Path Planning Algorithms
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Wind Aware Batch Informed Trees for Path Planning of Small UAS to Minimize Travel Time — Seung-Keol Ryu, Eric W. Frew, et al. · (2025) | TGRS Research Map | TGRS