Wind-Aware Optimal Trajectory Planning for Efficient Gliding of Fixed-Wing Aerial Systems

Autonomous fixed-wing flight is becoming a key capability in aerial robotics, enabling sensing, mobility, and contingency operations across both small-scale Uncrewed Aircraft Systems and large-scale Advanced Air Mobility. During unpowered operation in fixed-wing platforms, airspeed is regulated solely through potential-kinetic energy exchange, making airspeed dynamics highly sensitive to guidance commands, particularly under wind. This paper presents a viability-based airspeed protection for ground-referenced guidance in steady wind, where airspeed evolution depends explicitly on the commanded flight path angle. Leveraging Nagumo's tangency condition, we derive a closed-form, wind-dependent characterization of admissible guidance commands that guarantees forward invariance of a safe airspeed envelope. These conditions are embedded within an offline quadratic programming framework to certify airspeed-safe maneuver primitives for non-ascending flight at the guidance level. The approach is validated using a high-fidelity unpowered fixed-wing aircraft model on gliding trajectories formed by concatenating certified maneuver primitives, demonstrating strict airspeed boundedness. Future work will address unsteady wind fields and flight experiments.

Small multirotor aircraft are increasingly tasked with operations in the atmospheric boundary layer, where turbulent winds comparable to the vehicle's airspeed degrade trajectory tracking and can defeat conventional feedback control. This work illustrates a two-stage learning pipeline that first estimates the local wind from onboard kinematics and dynamics and then exploits that estimate inside a reinforcement learning (RL) flight controller. The wind estimator, an attention-augmented gated recurrent network trained on thousands of simulated flights through von Karman turbulence with power-law shear and veer, recovers the horizontal wind vector with a per-flight root-mean-square error of 0.40 m/s and a direction error of 3.2 degrees on unseen wind regimes, an accuracy near the floor imposed by unresolved turbulence, and generalizes to vertical ascent profiles with a skill score of 0.861 over a constant-wind reference. A proximal policy optimization controller receiving the frozen estimator's output reduces horizontal trajectory tracking error by 48% relative to a wind-blind proportional-derivative baseline across mean winds of 4 m/s to 12 m/s, winning on 100% of evaluation episodes. A three-way ablation decomposes this improvement into a kinematic component, available without wind information, and a wind-perception component; the perception share rises with wind speed, from small in light winds toward roughly half the total benefit in strong winds, consistent with the quadratic scaling of aerodynamic drag. The controller degrades gracefully on out-of-distribution winds of 13 m/s to 15 m/s, where the baseline fails catastrophically.

This work presents a motion planning framework for UAV navigation in non-convex urban air corridors. The planner is based on a mixed-integer tracking model predictive control formulation that enforces corridor feasibility and dynamic consistency within a single optimization problem. To guarantee convergence to the target and mitigate the occurrence of local minima induced by non-convex geometry, a shortest-path-based offset cost with feasibility constraints is embedded directly into the planning problem. Numerical simulations show that the proposed formulation generates dynamically valid trajectories that satisfy the corridor constraints and converge to the target without relying on external global planning stages.