AMRs use sensors such as LiDAR, cameras, IMUs, and ultrasonic sensors to perceive their environment and make navigation decisions. They process real-time data from these sensors using Simultaneous Localization and Mapping (SLAM) algorithms to build and update a map of their surroundings. AMRs use AI algorithms like A\* and D\* Lite for path planning and motion control, and machine learning models for object recognition and decision-making. To operate efficiently, AMRs rely on edge computing for real-time processing and cloud processing for large-scale data analysis. They also use fleet management systems to coordinate tasks and share data across fleets. However, businesses must address challenges such as multimodal data complexity, data storage and bandwidth constraints, latency in real-time processing, security and privacy concerns, scalability and data management for fleets, and training employees to interact with these robots.