Robotics Navigation Based on YOLO and Depth Camera

In recent years, significant advancements have been made in robotics, yet challenges remain in navigation, particularly for autonomous vehicles and bipedal robots. This paper provides a comprehensive review of three critical components in robotic navigation: YOLO neural networks, depth cameras, and A* path planning. Existing studies often address these aspects independently, lacking an integrated approach. Here, we systematically summarize and analyze the effectiveness of these techniques in navigation tasks. Through a literature review of recent publications, we compare and categorize various methods to assess trends in YOLO research and explore potential for integrated research in navigation. Furthermore, we analyze the strengths and limitations of each method in dynamic environments. The findings suggest that while YOLO and depth camera-based systems excel in real-time object detection and spatial awareness, they face challenges related to light sensitivity and high computational demands. Future research directions are proposed to enhance adaptability in complex environments, improve efficiency, and support cost-effective navigation solutions in robotics.