Topology-Aware Line Guidance for Warehouse MAVs: Lightweight Junction-Driven Navigation with Real-Time Path Encoding and Multi-Path Adaptation

This paper presents a vision-based navigation framework for micro air vehicles (MAVs) operating in confined warehouse environments. To address the trade-off between low localization accuracy in mapless methods and high computational demands in map-based approaches, the proposed system leverages topology-aware path guidance using monocular vision. Navigation is driven by a digital instruction format (DIF) that encodes both the path index and target junction, enabling autonomous navigation without environmental modifications. The framework comprises a cascaded perception–encoding–control pipeline. For structured paths, foreground pixel density trend analysis with sliding window smoothing for robust junction recognition, while lateral proportional-integral-derivative (PID) control ensures accurate path tracking. For geometric trajectories, the control logic incorporates L-junction triggers, fixed-angle turns, and spatial yaw correction to accommodate sharp corners and curved segments. ROS-Gazebo simulations validate the method’s effectiveness, achieving up to 94.40% junction recognition accuracy (92.01% on average), trajectory tracking errors below 0.1 m, and terminal localization deviations under 0.2 m. These results validate the method’s accuracy, stability, and suitability for computationally constrained MAV platforms in warehouse automation.