<![CDATA[Traditional automated guided vehicles (AGVs) are restricted by their reliance on predefined paths, limiting adaptability in dynamic warehouse environments. While autonomous mobile robots (AMRs) overcome this limitation through on-board simultaneous localization and mapping (SLAM) and autonomous navigation, standard configurations often suffer from top mounted-sensor blind spots when loads are carried on the chassis. To address these coverage gaps, an indoor logistic AMR based on the robot operating system 2 (ROS2) was designed and evaluated. The platform was developed by combining a multi-LiDAR perception stack with low-cost industrial actuation and a lightweight, fleet-style user interface. Within the system architecture, data from two light detection and ranging (LiDAR) sensors were merged at the topic level into a single virtual scan for SLAM toolbox and Nav2. Additionally, actuation and wheel odometry were driven by an RS-485 Modbus-based brushless DC (BLDC) motor controller, while ultrasonic sensors for short-range safety, an inertial measurement unit (IMU) for orientation, and a network of microcontroller calling stations communicating via message queuing telemetry transport (MQTT) were integrated into the platform. Experimental validation demonstrated successful multi-LiDAR fusion, with the Modbus motor driver achieving a motion-control error of 0.36 % and a speed-retrieval error of 0.43 %. Furthermore, calling-station commands were reliably executed over MQTT, and a point-to-point navigational repeatability of 10.3 cm was achieved. These findings indicate that an integrated multi-LiDAR ROS2 AMR provides a highly practical solution for indoor logistics. Through the proposed sensor merger and calling-station handshake, two recurring vulnerabilities of standard ROS2 deployments—single-LiDAR coverage gaps and Nav2 goal-overwriting behavior—were successfully resolved.]]>
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