Adaptive long-range UAV flight planning using Monte Carlo search trees

An adaptive Monte Carlo tree search (MCTS) framework is presented for long-range, energy-aware trajectory planning and online re-planning of fixed-wing uncrewed aerial vehicles in dynamic environments. Mission planning is posed as a finite-horizon Markov decision process with a four-degree-of-freedom kinematic and energy model, 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 that approximately satisfy altitude, obstacle, and airspace constraints. Simulations for a coastal medical delivery mission show that, relative to a Batch Informed Trees baseline, the method trades modest path-length increases for reduced energy consumption and, under emerging storm-front no-fly regions, rapidly computes safe diversion trajectories that preserve most nominal energy performance under fixed computational budgets.