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

Abstract

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.

Keywords

lightweight navigation technique / junction recognition / multi-path adaptation strategy / topology-aware line guidance / warehouse logistics automation