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

TL;DR

This paper introduces a nonlinear trajectory planning method for fixed-wing UAVs that optimizes gliding efficiency under wind disturbances, integrating real-time re-planning and energy estimation.

Contribution

It presents a novel multi-cost trajectory planner using Bernstein polynomials and differential flatness, enabling online re-planning for wind-aware UAV gliding.

Findings

Validated in CFD simulations and real-world experiments.

Achieves stable sink rate, airspeed, and glide ratio under wind and obstacles.

Enables hybrid powered and unpowered flight trajectories.

Abstract

Gliding offers small fixed-wing UAVs extended endurance and silent operation but requires accurate energy management, especially under wind disturbances and obstacle constraints. Traditional Total Energy Control Systems based controllers regulate the trade between potential and kinetic energy reactively, often requiring fine-tuning and trim-conditions knowledge. In this work, we shift the regulation to the planning level and present a nonlinear, multi-cost trajectory planner for small UAV gliders. The method generates $\mathcal{C}^3$ continuous trajectories based on Bernstein polynomials, mapped into control commands through differential flatness, and re-planned online to match experimentally derived sink polar curves. A simulated netto variometer is integrated into the optimization to estimate air mass motion, constraining the glide to energy-balanced states. Consecutive gliding trajectories are linked by cruising segments computed through trajectories initialized on Dubins path-based waypoints, enabling hybrid missions that combine powered and unpowered flight. The approach is validated in CFD simulations and real-world experiments with a fixed-wing platform, showing reliable stabilization of sink rate, airspeed, and glide ratio under wind gusts and in presence of obstacles.