Autonomous Navigation Technology for Robots and Its Applications in Intelligent Transportation and Industrial Fields

This paper delves into the pivotal role of LiDAR, stereo cameras, RGB cameras, and Simultaneous Localization and Mapping (SLAM) in enhancing environmental perception for autonomous systems. It elucidates the fundamental principles underlying these technologies and their critical applications in autonomous driving and robotics. The paper underscores how LiDAR provides high-resolution 3D maps crucial for obstacle detection and collision avoidance, while stereo cameras leverage parallax to measure depth, facilitating navigation and obstacle detection in robotics and autonomous vehicles. RGB cameras, though lacking direct depth measurement, are invaluable for colour-based object recognition and tracking. SLAM is highlighted for its ability to construct maps of unknown environments in real-time, essential for autonomous navigation in dynamic settings. The paper also discusses path planning technologies such as the Dynamic Window Approach (DWA), A*, and Rapidly Exploring Random Tree (RRT), which are integral to the navigation capabilities of autonomous systems. These technologies collectively contribute to the advancement of intelligent transportation, industrial automation, and logistics by improving the safety, accuracy, and efficiency of autonomous operations.