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.

翻译：多旋翼无人机因电池限制面临飞行时间受限的问题。通过在飞艇上实现自主对接、机载电池充电和数据卸载，为延长无人机任务时间提供了一种有前景的解决方案。然而，飞艇易受阵风影响导致轨迹偏移，需要采用精确且具备障碍物感知能力的对接策略。为此，本研究提出两项关键创新：(i) 一种时序卷积网络，用于预测飞艇对阵风的响应，实现快速阵风检测并估计阵风效应消退的位置点；(ii) 一种模型预测控制器（MPC），利用这些预测计算无碰撞对接轨迹，该方法通过一种新型近距离避障方法实现飞艇近端机动。仿真结果表明，在不同场景下，本方法显著优于基于飞艇恒定速度模型的基线方法。我们进一步通过真实环境实验验证了该方案，首次在仿真环境外实现了多旋翼无人机对飞艇的自主对接控制策略。源代码发布于：https://github.com/robot-perception-group/multi_rotor_airship_docking。