提出风扰下无人机自主对接飞艇的新方法，提升续航与稳定性。
Autonomous Docking of Multi-Rotor UAVs on Blimps under the Influence of Wind Gusts
- 用时序卷积网络预测飞艇风扰响应，快速识别风停时刻。
- 基于预测结果设计无碰撞对接轨迹，实现在近距离避障。
- 首次在真实场景验证飞艇上无人机自主对接，适用于长时任务。
多旋翼无人机因电池限制飞行时间短。在飞艇上实现自主对接以完成充电和数据卸载，是延长无人机任务的有效方案。然而，飞艇易受风扰影响导致轨迹偏移，需具备障碍物感知能力的精确对接策略。本文提出两项创新：(i) 采用时序卷积网络预测飞艇对风扰的响应，实现快速风扰检测，并估计风扰影响消退的时刻；(ii) 设计模型预测控制器（MPC），利用上述预测结果计算无碰撞对接轨迹，引入一种新型近距离机动避障方法。仿真结果表明，该方法在多种场景下显著优于基于恒定速度假设的基线模型。进一步在真实实验中验证了该策略，首次实现了飞艇上多旋翼无人机自主对接的非仿真演示。源代码已公开：https://github.com/robot-perception-group/multi_rotor_airship_docking。
原文摘要 · Abstract (English)
Multi-rotor UAVs face limited flight time due to battery constraints. Autonomous docking on blimps with onboard battery recharging and data offloading offers a promising solution for extended UAV missions. However, the vulnerability of blimps to wind gusts causes trajectory deviations, requiring precise, obstacle-aware docking strategies. To this end, this work introduces two key novelties: (i) a temporal convolutional network that predicts blimp responses to wind gusts, enabling rapid gust detection and estimation of points where the wind gust effect has subsided; (ii) a model predictive controller (MPC) that leverages these predictions to compute collision-free trajectories for docking, enabled by a novel obstacle avoidance method for close-range manoeuvres near the blimp. Simulation results show our method outperforms a baseline constant-velocity model of the blimp significantly across different scenarios. We further validate the approach in real-world experiments, demonstrating the first autonomous multi-rotor docking control strategy on blimps shown outside simulation. Source code is available here https://github.com/robot-perception-group/multi_rotor_airship_docking.
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