InSystems and Model Engineering Solutions jointly developed a Simulink model for an adaptive transport robot fleet. The fleet is based on InSystems’ proANT robots. The goal is to accurately represent adaptive system behavior and effectively address typical challenges of Collaborative Embedded System Groups (CSGs).
Article has been previously featured in Robotic Magazine.
In modern manufacturing environments, the interaction of multiple autonomous robots must be both flexible and robust. Production lines need to respond quickly to changing order priorities, fluctuating volumes, or the failure of individual robots - without compromising overall efficiency.
To achieve this level of adaptability and robustness, InSystems and Model Engineering Solutions developed a Simulink model of a fleet of proANT collaborative transport robots. The model represents the behavior of a Collaborative Embedded System Group (CSG) and serves as the foundation for the complete model-based development (Model-Based Development, MBD) of the automation solution.
A robot fleet must continuously respond to dynamic changes. These include adjustments in the control logic of the manufacturing execution system as well as changes in the number and characteristics of participating systems.
This dynamic behavior increases complexity in both development and operation. Traditional development approaches quickly reach their limits, particularly in terms of traceability, testability, and scalability.
Model-Driven Software Development (MDSD / MDD) provides a structured approach to managing this complexity.
By specifying the CSG as executable models, a fully virtual and simulated representation of the robot fleet is created. This enables early analysis, validation, and targeted optimization of system behavior.
A key advantage of model-based development is the reuse of models and test environments across different development phases. Once created, components can be leveraged throughout the lifecycle.
Especially in complex, adaptive systems, reusability is a critical factor for long-term project success.
Another success factor is the seamless integration of the toolchain, enabling a high degree of automation in core development activities:
Quality assurance in particular benefits from model-based approaches. Tools such as the MES Model Examiner® (MXAM) help ensure compliance with modeling guidelines, analyze model structures, and evaluate relevant model metrics.
The combination of modeling, simulation, and automated analysis provides a solid foundation for developing adaptive robotic systems. Changes in system behavior can be evaluated early and implemented efficiently.
This not only reduces development time but also leads to sustainable improvements in system quality.
MES Model Examiner® (MXAM) provides a convenient all-in-one solution for checking modeling guidelines, analyzing model structure, and evaluating model metrics. In this webinar, we explored how MXAM supports comprehensive static analysis for models created with Simulink, Stateflow, Embedded Coder, and TargetLink.
