A Bi-directional Adaptive Framework for Agile UAV Landing

2026-01-06

arXiv.org

AI Summary

This work proposes a bidirectional cooperative landing framework to address the inefficiency and poor robustness of conventional “track-then-descend” paradigms for autonomous drone landing on moving platforms in highly dynamic scenarios. By modeling the drone and the mobile platform as a coupled system, the approach enables the platform to actively adjust its pose to provide stable terminal conditions while the drone simultaneously generates time-optimal and energy-efficient trajectories. This parallelization of alignment and descent phases overcomes the limitations of single-agent passive tracking by treating the mobile platform as an active collaborator for the first time. Joint optimization of both agents significantly enhances landing precision, efficiency, and robustness, enabling rapid state synchronization and aggressive trajectory tracking.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

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.

Problem

• autonomous landing
• mobile platforms
• quadcopter
• dynamic scenarios
• cooperative landing

Innovation

• bi-directional cooperation
• coupled system optimization
• active mobile platform
• agile UAV landing
• time-optimal trajectory