Social Navigation in Warehouse Logistics Based on Artificial Intelligence and RGB-D

Purpose: Ensuring both human safety and transportation efficiency simultaneously during the navigation of autonomous mobile robots (AMRs) in warehouse logistics is a challenging problem due to dynamic environments and diverse obstacles. In this study, a social navigation approach based on artificial intelligence was developed to optimize these two critical factors. Methodology: RGB images from an Intel_RealSense_D455 depth camera mounted on the PIXER AMR were utilized in a YOLOv8-based model to detect humans and reach trucks (RT). For human detection, the YOLOv8 model was trained with 4746 images and labels for 362 epochs, while RT detection used 4193 images and labels for 450 epochs. Each dataset was split into 60% training, 20% testing, and 20% validation subsets. The depth feature of the camera was used to measure object distances. Findings: Objects detected with at least 80% accuracy had their midpoints identified, and distances were calculated using the depth camera. For humans detected within 2 meters, the robot's max_speed was reduced to 80%. For RTs detected at 6 meters, a new path was planned. Originality: This study provides a novel integration of social navigation and deep learning to address the dual challenge of ensuring safety and efficiency in AMR navigation, contributing to advancements in warehouse logistics.