Autonomous Wingtip Docking of Fixed-Wing UAVs: Relative Navigation, Closed-Loop Control, and System-Level Challenges

Yulong Zhang; Wei Zhou; Jing Zhou; Peiyang Ma; Qiuyan Wang

doi:10.6919/ICJE.202608_12(8).0007

Authors

Yulong Zhang Department of Rocket Force University of Engineering, Xi'an, China
Wei Zhou Department of Rocket Force University of Engineering, Xi'an, China
Jing Zhou Department of Rocket Force University of Engineering, Xi'an, China
Peiyang Ma Department of Rocket Force University of Engineering, Xi'an, China
Qiuyan Wang Department of Rocket Force University of Engineering, Xi'an, China

DOI:

https://doi.org/10.6919/ICJE.202608_12(8).0007

Keywords:

Fixed-wing UAV; Wingtip Docking; Relative Navigation; Visual–inertial Fusion; Visual Servoing.

Abstract

Autonomous wingtip docking of fixed-wing unmanned aerial vehicles offers a potential approach to long-endurance flight, modular mission reconfiguration, and airborne assembly. Unlike conventional formation flight, this task requires two forward-flying aircraft to reduce the relative position, attitude, and closing-velocity errors between their actual docking points while satisfying fixed-wing flight-envelope, visibility, aerodynamic-interaction, and collision-avoidance constraints. This article organizes relevant research around two closely coupled themes: relative navigation and closed-loop docking control. Vision-based relative measurement methods are examined, including cooperative fiducial markers, structure-based non-cooperative perception, monocular and stereo pose estimation, and learning–geometry hybrid approaches; multi-sensor relative-state estimation is then discussed with emphasis on visual–inertial fusion, GNSS/RTK and UWB assistance, dual-moving-platform modeling, asynchronous measurements, lever-arm compensation, observability, and uncertainty representation. The control literature is further analyzed from the perspectives of position-based and image-based visual servoing, robust and prescribed-performance control, model predictive and safety-critical control, and learning-based disturbance compensation. The central technical challenge lies not in improving pose-estimation accuracy or tracking convergence in isolation, but in constructing an uncertainty-aware, constraint-compliant, and contact-transition-capable autonomous closed-loop system. Future research should therefore focus on dual-platform relative observability, confidence-aware closure control, visibility-preserving trajectory planning, real-time near-field aerodynamic modeling, hybrid contact dynamics, navigation–control–mechanism co-design, and progressively structured experimental validation.

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