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

2025-11-24

AI Summary

To address the limited mission duration of multirotor UAVs due to battery constraints and the degraded docking accuracy of airship platforms under gust disturbances, this paper proposes an autonomous docking method for dynamic airships. Methodologically, it introduces a novel time-convolutional-network (TCN)-based gust response modeling and attenuation-point prediction mechanism, integrated with model predictive control (MPC) and near-field active obstacle avoidance to realize a closed-loop perception–planning–control architecture. Contributions include: (i) the first real-world, non-simulated autonomous docking of a multirotor UAV onto a moving airship; (ii) significantly improved docking accuracy and robustness in highly dynamic, cluttered environments; and (iii) concurrent support for battery recharging and data offloading, effectively extending mission duration. Simulation and flight experiments demonstrate superior performance over constant-velocity baseline approaches.

Technology Category

• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Intelligent Robots: State Estimation

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Autonomous docking of UAVs on blimps under wind gusts
• Predicting blimp responses to wind gusts for docking
• Computing collision-free trajectories for UAV-blimp docking

Innovation

• Temporal convolutional network predicts blimp wind responses
• Model predictive controller computes collision-free docking trajectories
• Novel obstacle avoidance method for close-range blimp maneuvers