A Bi-directional Adaptive Framework for Agile UAV Landing

TL;DR

This work tackles autonomous quadrotor landing on mobile platforms under dynamic disturbance by introducing a bi-directional cooperative framework that actively engages the platform in the landing process. A two-stage planning pipeline first computes an optimal terminal state, including the platform tilt $\phi_{opt}$ and the quadrotor final attitude, then generates a time- and energy-efficient 3D minimum-jerk trajectory to reach those targets. The platform employs active attitude control to shape the feasible trajectory space, enabling large-maneuver, time-critical landings within transient windows; the quadrotor uses a nonlinear controller to track the optimized path, while a coupled visual feedback mechanism ensures robust alignment. Across simulation and outdoor experiments, the approach achieves higher efficiency, precision, and robustness compared to baseline methods, demonstrating the practical potential of bi-directional cooperation for rapid quadrotor recovery in complex environments.

Abstract

Autonomous landing on mobile platforms is crucial for extending quadcopter operational flexibility, yet conventional methods are often too inefficient for highly dynamic scenarios. The core limitation lies in the prevalent ``track-then-descend'' paradigm, which treats the platform as a passive target and forces the quadcopter to perform complex, sequential maneuvers. This paper challenges that paradigm by introducing a bi-directional cooperative landing framework that redefines the roles of the vehicle and the platform. The essential innovation is transforming the problem from a single-agent tracking challenge into a coupled system optimization. Our key insight is that the mobile platform is not merely a target, but an active agent in the landing process. It proactively tilts its surface to create an optimal, stable terminal attitude for the approaching quadcopter. This active cooperation fundamentally breaks the sequential model by parallelizing the alignment and descent phases. Concurrently, the quadcopter's planning pipeline focuses on generating a time-optimal and dynamically feasible trajectory that minimizes energy consumption. This bi-directional coordination allows the system to execute the recovery in an agile manner, characterized by aggressive trajectory tracking and rapid state synchronization within transient windows. The framework's effectiveness, validated in dynamic scenarios, significantly improves the efficiency, precision, and robustness of autonomous quadrotor recovery in complex and time-constrained missions.

Figures (8)

• Figure 1: Cooperative landing process on a mobile variable-attitude platform.
• Figure 2: The process occurs in two stages. First, in Stage One, the quadcopter obtains information from the platform to calculate its landing attitude and synchronizes this data back. Then, in Stage Two, the quadcopter plans its trajectory and begins to land as the platform simultaneously adjusts its own attitude to facilitate the landing.
• Figure 3: The framework diagram of the coordinated landing system of the quadcopter and the landing platform. The quadrotor calculates the desired landing attitude based on its own constraints, and the platform can actively adjust the angle to adapt to the quadcopter's attitude during landing.
• Figure 4: Simulation experiment results. (a) Trajectory of the quadrotor and the landing platform at different vehicle speeds. The platform moving speeds from inside to outside are $0.8\,\text{m/s}$, $1.0\,\text{m/s}$, $1.3\,\text{m/s}$, $1.5\,\text{m/s}$, and $2.0\,\text{m/s}$. (b) Afterimage of the Quadcopter (red frame) landing on the variable moving platform (yellow dotted line) at a speed of $1.0\,\text{m/s}$, and successfully landing on the platform before the AGV enters the rough ground.
• Figure 5: Performance comparison of three different landing strategies at an AGV speed of $1.0\,\text{m/s}$, where solid lines represent the quadrotor trajectory and dashed lines represent the landing platform trajectory.