Implementation of AutomationML for Flexible Programming for Behaviour Changing between Similar Robots

See, Zhang Ming (2026) Implementation of AutomationML for Flexible Programming for Behaviour Changing between Similar Robots. Final Year Project (Bachelor), Tunku Abdul Rahman University of Management and Technology.

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Abstract

In the era of Industry 4.0, adaptability, modularity, and real-time responsiveness have become critical features of modern automation systems. Traditional microcontroller-based robots, such as those using Arduino, often lack the flexibility to dynamically reconfigure their behaviours in response to changing operational demands without manual reprogramming. This project addresses that limitation by integrating AutomationML (Automation Markup Language) into a lightweight robotic platform to achieve behaviour-level adaptability. The system developed uses an Arduino 2WD drive robot enhanced with an ESP32 microcontroller to receive commands based on AutomationML data. A middleware layer was designed to parse AML files and transmit corresponding instructions to the robot, enabling real-time switching between behaviours such as obstacle avoidance and line following. Additionally, a cloud-based datalogging mechanism was implemented using the ESP32’s Wi-Fi capabilities to periodically upload performance metrics and sensor data to Google Sheets, supporting remote monitoring and evaluation. The results demonstrate that an arduino robot can be transformed into a flexible and reconfigurable robotic system when combined with structured data models and cloud integration. This work not only highlights the feasibility of using AutomationML beyond industrial-grade platforms but also contributes a practical framework for introducing Industry4.0 principles, such as interoperability, reusability, and scalable automation, into educational and prototyping environments