Autonomous Docking of Multi-Rotor UAVs on Blimps under the Influence of Wind Gusts

TL;DR

This paper presents a novel autonomous docking strategy for multi-rotor UAVs on blimps, using wind response prediction and model predictive control to ensure precise, obstacle-aware docking in windy conditions, validated through simulation and real-world experiments.

Contribution

Introduces a temporal convolutional network for wind gust prediction and a model predictive controller for obstacle-aware docking, advancing autonomous UAV-blimp docking capabilities.

Findings

Outperforms baseline constant-velocity models in simulations

Successfully demonstrated autonomous docking in real-world experiments

Enhances robustness of UAV docking under wind gust disturbances

Abstract

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.