Wind-Aware Optimal Trajectory Planning for Efficient Gliding of Fixed-Wing Aerial Systems
Luca Morando, Nishanth Bobbili, Luca Masci, Giuseppe Loianno
AI summary
Problem
Small fixed-wing UAVs rely on gliding for extended endurance but struggle with precise energy management under wind disturbances and obstacle constraints. Traditional reactive controllers require extensive tuning and lack predictive capabilities to maintain stable, energy-balanced glide paths.
Approach
The authors propose a nonlinear, multi-cost trajectory planner parameterized by Bernstein polynomials that explicitly incorporates wind estimation and a simulated netto variometer constraint. This planner generates smooth, dynamically feasible paths that are continuously replanned online and mapped to control commands via differential flatness.
Key results
- Minimum-jerk, time-optimal glide path generation respecting dynamic and curvature constraints
- Integration of a variometer model as a Bernstein polynomial constraint for accurate sink rate prediction
- Continuous online replanning strategy that adapts to real-time wind estimates and obstacle avoidance
- Experimental validation demonstrating reliable stabilization of sink rate, airspeed, and glide ratio under wind gusts
Why it matters
Shifts energy management from reactive control to predictive planning, enabling safer, longer-endurance missions for small fixed-wing UAVs in complex, windy environments.
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 en- ergy 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 C3 continuous tra- jectories 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.