Experimental validation of vision-based hybrid navigation for differential-drive robots

Abstract

Reliable mobile-robot deployment requires mapping, localization, global planning, and real-time obstacle avoidance to operate consistently under sensor noise and physical constraints. This study presents a vision-based hybrid navigation framework for a differential-drive mobile robot in a warehouse-like environment. The main contribution lies in the system-level integration and experimental validation of established techniques rather than the development of a new standalone navigation algorithm. A ceiling-mounted camera converts top-view images into an occupancy-grid map and world coordinates, while a convolutional neural network (CNN) recognizes goal markers. Odometry–augmented reality University of Cordoba (ArUco) fusion is used to correct accumulated localization drift. Particle swarm optimization (PSO) generates smooth global paths offline, whereas the dynamic window approach (DWA) performs real-time local motion control. Experiments in a 3.3 m × 2.4 m workspace achieved average obstacle-localization errors of 0.959 cm and 0.696 cm along the x- and y-axes, respectively, and a goal-recognition accuracy of 99%. The DWA controller required 12.61±2.42 ms per cycle, while rapidly-exploring random tree (RRT) and PSO required 4–7 s and 477–692 s, respectively, for global path generation. Simulation and physical experiments confirmed collision-free navigation and successful quick response (QR)-code-based goods inspection, demonstrating the feasibility of the proposed framework for small, structured indoor environments.