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arXiv · 2610.25804

Adaptive Long-range UAV Flight Planning using Monte Carlo Search Trees

Chi Keng Dong·Justin Kernot·Dominic Liao-McPherson
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Abstract

Long-range fixed-wing UAV missions require energy-efficient, dynamically feasible trajectories that can adapt online to evolving wind and airspace constraints. We present a Monte Carlo Tree Search (MCTS)-based framework that addresses this challenge by formulating mission planning as a finite-horizon Markov decision process incorporating aircraft kinematics, onboard energy dynamics, forecast wind fields, terrain, and time-varying no-fly zones. The MCTS planner searches over waypoint references generated by pre-stabilizing feedback controllers and a constraint-aware navigation field, yielding dynamically feasible trajectories. Simulation results from medical delivery scenarios show that the MCTS planner outperforms baseline geometric planners and population-based optimization algorithms. Moreover, the MCTS planner can be used in closed-loop to rapidly compute safe diversion trajectories under evolving constraints.

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Chi Keng Dong, Justin Kernot, Dominic Liao-McPherson. 2026-10-19. Adaptive Long-range UAV Flight Planning using Monte Carlo Search Trees. https://doi.org/10.2514/1.g010042

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