Integration of AGV and Digital Twin: Applications and Implications

Integration of AGV and Digital Twin:Applications and Implications

Abstract:

In response to the strategic transitionfrom a manufacturing powerhouse to a manufacturing leader, proposed in 2015,emphasizing the deep integration of new-generation information technology withmanufacturing and advancing intelligent manufacturing, strengthening thedevelopment and application of Cyber-Physical Systems (CPS) is identified asone of the main directions. In recent years, the concept of Digital Twin hasalmost replaced CPS. This paper presents the author's simple understanding ofDigital Twin and explores its application in AGV systems.

Introduction:

Automated Guided Vehicle (AGV) is a type ofunmanned transportation equipment capable of autonomously operating indoors oroutdoors according to preset programs or navigation systems and performingvarious handling operations. With the development of automation and informationtechnology, AGVs have been widely used in multiple industries such asmanufacturing, logistics, pharmaceuticals, and food, undertaking tasks such asmaterial handling, production line integration, and warehouse management.

Digital Twin is the creation of a virtualmodel of physical objects, systems, or processes throughout their entirelifecycle in a digital environment. This model can reflect the state, behavior,and performance of physical objects and, through real-time data interaction,simulate various application scenarios of physical objects for predictiveanalysis, optimization design, and remote monitoring.

Integration of AGV and Digital Twin:

During the design phase: Digital Twintechnology can assist engineers in building a virtual model of AGVs in theearly design stage, simulating their operating states and performance indifferent environments, optimizing mechanical structures, navigationalgorithms, and energy consumption parameters.

Virtual debugging and testing: The controlsystem and operational logic of AGVs can be pre-tested in a Digital Twinenvironment, greatly reducing problems and costs during actual deployment.

Operational optimization: By utilizingDigital Twin technology, AGV behavior in complex logistics systems, such aspath planning, task scheduling, and obstacle avoidance strategies, can be simulated,thereby optimizing overall material handling efficiency and resourceutilization.

Real-time monitoring and maintenance:Real-time acquisition of AGV operating data and feedback to the Digital Twinmodel allows the system to provide early warnings and guide maintenancepersonnel to perform precise and rapid fault diagnosis and repairs when AGVsencounter malfunctions or require maintenance.

On-site dynamic adjustments:

During the production process, ifunexpected situations arise (such as temporary increases in demand on aproduction line or equipment failures), the Digital Twin system can recalculatethe optimal AGV scheduling strategy based on real-time changes and dynamicallyadjust AGV operating paths and task assignments.

Conclusion:

Therefore, Digital Twin technology is ofsignificant importance in enhancing the intelligence of AGVs, improving theiradaptability in complex environments, and enhancing the lean management levelof the entire production and logistics system.