Real-Time Rack Monitoring for Safer AS/RS Operation

Research Background

With the rapid development of e-commerce and modern warehousing logistics, Automated Storage And Retrieval Systems and large-scale racking systems are being widely used. As the main load-bearing structure in a warehouse, racking safety directly affects operational safety, equipment stability, and warehouse efficiency.

Rack deformation, tilting, or collapse may cause cargo damage and serious safety accidents. In many cases, these risks are related to excessive rack verticality or levelness deviation that has not been detected and handled in time.

Traditional rack inspection mainly relies on manual periodic checks. Manual inspection has several limitations, including long inspection cycles, high error rates, missed detection risks, high labor cost, and delayed abnormal reporting.

Therefore, it is important to build an intelligent system that can automatically, continuously, and accurately monitor rack verticality and levelness, trigger alarms when abnormalities occur, and notify relevant personnel in real time by email and SMS.

Research Objectives

Overall System Design

The system adopts a three-layer architecture: perception layer, network layer, and application layer.

Perception layer: consists of sensor nodes installed on key rack columns and beams. Each node includes a MEMS IMU, a low-power MCU, and a wireless communication module. These nodes collect rack tilt data and perform local preprocessing.

Network layer: includes an on-site gateway and cloud server connection. The gateway collects data from sensor nodes, performs protocol conversion, and uploads data to the cloud through 4G or Ethernet. It also supports local alarm logic, so alarms can still be triggered when the network is interrupted.

Application layer: is deployed on the cloud server. It is responsible for data storage, threshold management, alarm decision-making, and notification sending. Users can monitor rack status, query historical data, and configure parameters through a web interface or mobile app.

Core System Modules

The sensor node handles tilt angle acquisition and local filtering through MPU6050 and STM32L4.

The on-site gateway handles data aggregation, protocol conversion, and local alarms through Raspberry Pi 4B and LoRaWAN.

The cloud server supports data storage, alarm decision-making, and user management through Linux, MySQL, and Python.

The email notification module uses SMTP with TLS encryption. The SMS notification module uses a third-party SMS API. The user interface provides real-time monitoring and historical query through a web and Vue3 frontend.

Data Acquisition and Transmission

Sensor nodes collect raw IMU data at 10 Hz. After Kalman filtering, the processed tilt angle data is uploaded to the gateway at 1 Hz.

The data frame includes node ID, timestamp, verticality value, levelness value, battery level, and CRC16 checksum. LoRa communication uses a star topology, where each sensor node communicates directly with the gateway.

After data aggregation, the gateway sends data to the cloud server through MQTT. The cloud server stores the data and triggers real-time alarm judgment.

Alarm Notification Mechanism

The system supports three alarm levels.

Warning alarms: use yellow status and trigger app push plus email notification. Alarm-level warnings: use orange status and trigger email plus SMS notification. Severe alarms: use red status and trigger email, SMS, and voice call notification.

When an alarm is triggered, the system automatically sends key information such as rack number, warehouse location, tilt angle, alarm level, and timestamp.

To improve reliability, the system also supports retry mechanisms, repeated alarm suppression, and alarm escalation. For example, if an orange alarm is not confirmed within 15 minutes, it can be upgraded to a red alarm.

Software Design

The sensor node firmware is developed based on FreeRTOS and includes data acquisition, data processing, communication, local alarm, and power management tasks.

The cloud server adopts a microservice architecture, including data ingestion service, alarm engine service, notification service, and API service.

Time-series data is stored in InfluxDB, while user information, equipment information, configuration parameters, and notification records are stored in MySQL.

The web management interface provides real-time monitoring, alarm management, device management, historical query, and notification configuration. The mobile app supports key monitoring and alarm functions on iOS and Android.

Research Conclusion

This system provides an automatic detection and alarm notification solution for rack verticality and levelness.

By using MEMS IMU sensor fusion and Kalman filtering, the system achieves high-precision rack tilt detection. The three-layer architecture and LoRa wireless communication support reliable data acquisition and transmission. A single gateway can support up to 64 monitoring nodes.

The system also establishes a multi-level alarm mechanism, supports automatic email and SMS notifications, and provides real-time monitoring through web and mobile interfaces.

In practical warehouse operation tests, the system showed stable and reliable performance. It provides an effective technical solution for improving rack safety, reducing manual inspection risks, and supporting the long-term stable operation of Automated Warehouse Systems.

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