The rapid growth of Industry 4.0 technologies has had a significant impact on warehouse operations. Because of this, intelligent, adaptable, and autonomous systems must now be able to work well in places where things are always changing. Autonomous Mobile Robots (AMRs) are now a key way to automate tasks like moving things, keeping track of stock, and running the logistics process. But most traditional AMR systems use static navigation methods and can't change to deal with moving individuals or objects in real time. This rule makes stores less safe and less efficient.

This study presents an AI-driven Autonomous Mobile Robot integrated with vision-based Simultaneous Localization and Mapping (SLAM) for intelligent navigation in warehouses. The proposed system employs both LiDAR and camera-based perception techniques to generate real-time occupancy grid maps and ascertain the robot's precise location in intricate environments. A* is used in a path planning algorithm to find the best routes for navigation. A new risk assessment module that uses artificial intelligence checks the environment all the time and changes how the robot acts when it needs to.