为小型固定翼无人机设计风感知滑翔轨迹规划，提升续航与稳定性。
Wind-Aware Optimal Trajectory Planning for Efficient Gliding of Fixed-Wing Aerial Systems
- 基于伯恩斯坦多项式生成平滑轨迹，利用微分平坦性转为控制指令。
- 在线重规划匹配实测下沉率曲线，实现风扰下稳定滑翔与能量平衡。
- 支持动力与滑翔混合任务，适合需长续航的野外无人机应用。
滑翔可延长小型固定翼无人机的续航并实现静音运行，但需精确的能量管理，尤其在风扰和障碍物约束下。传统总能量控制系统基于反馈调节势能与动能，常需精细调参及配平条件知识。本文将调控机制前移至规划层面，提出一种非线性多目标轨迹规划方法，用于小型无人机滑翔。该方法基于伯恩斯坦多项式生成$/mathcal{C}^3$连续轨迹，通过微分平坦性映射为控制指令，并在线重规划以匹配实验获得的下沉极曲线。优化中集成模拟净变速率仪，用于估计气团运动，约束滑翔至能量平衡状态。连续滑翔轨迹通过基于杜宾路径的初始航点计算巡航段连接，实现动力与无动力飞行的混合任务。该方法在CFD仿真与真实飞行平台中验证，可在风切变和障碍物存在条件下可靠稳定下沉率、空速与滑翔比。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。