HEA-Planner: A Hierarchical Environment-Aware Trajectory Planning Framework for Autonomous UAV Landing on Moving Platforms in Confined Dynamic Warehouse Environments

Autonomous quadrotor landing on moving ground platforms is essential for cooperative air–ground operations in confined warehouse environments. However, existing trajectory planners often compromise corridor clearance in narrow aisles or fail to track maneuvering platforms under dynamic intrusions. To address these challenges, a hierarchical environment-aware trajectory planning framework (HEA-Planner) is presented. At the global layer, an Environment-Aware Ant Colony Optimization (EA-ACO) algorithm is formulated to generate corridor-centered reference paths using environmental feedback. At the local layer, an Environment-Aware A* (EA-A*) replanner is designed to coordinate reactive obstacle avoidance and platform interception via spatiotemporal risk repulsion and predictive attraction fields. At the trajectory optimization layer, a Spatially-Guided MINCO (SG-MINCO) solver is introduced to preserve corridor clearance, while a Spatiotemporal Coupled MINCO (STC-MINCO) solver is derived to resolve moving-platform rendezvous through analytical duration total derivatives. Simulations across three operational states demonstrate robust collision avoidance and dynamic synchronization. Furthermore, physical flight experiments validate onboard closed-loop execution, achieving an average replanning latency of 33.6±4.9ms and a terminal touchdown error of 148.2±37.3mm across all trials (100% success rate), verifying the real-time feasibility of autonomous air–ground handover in restricted logistics workspaces.