Beyond the Blueprint: How We Built a High-Fidelity Robot Control System with JaamSim

Welcome to the Linebridge Solutions blog, where we explore the practical side of intralogistics and automation. To kick things off, we’re taking you behind the scenes of a project to show you how we move from concept to a fully validated solution. The secret lies in rigorous, intelligent simulation.

For this, one of our most powerful tools is JaamSim.

What is JaamSim? A Simulator for Complex Realities

In the world of logistics and manufacturing, off-the-shelf solutions rarely fit perfectly. JaamSim, an open-source discrete-event simulation software, gives us the flexibility to build what others can't. Its key advantage is extensibility—it allows us to create highly customized models that mirror the unique logic of a client's operation, right down to integrating with external programming languages like Python.

This capability was essential in a recent material handling automation project for a food manufacturing client.

The Challenge: Dynamic Material Flow in a Food Factory

The scenario was a classic but complex one: a busy factory floor with four different types of raw materials. Each material needed to be transported from a specific pickup point to a designated drop-off zone, and each followed its own unique production rhythm.

The core challenge wasn't just moving materials; it was designing an intelligent Robot Control System (RCS) to manage a fleet of Automated Guided Vehicles (AGVs) in a dynamic environment. The system needed to answer critical questions in real-time: Which task is most urgent? Which AGV is best positioned to handle it? How do we ensure seamless, just-in-time delivery without creating traffic jams?

Our approach was to build and validate the entire system in a digital environment first.

Our Solution: A Custom-Built Robot Control System in JaamSim

We engineered a high-fidelity digital twin of the factory's material handling process. The centerpiece of this model was the custom RCS, designed to orchestrate the AGV fleet with precision. Here’s a step-by-step breakdown of its architecture:

1. The Task Triggering Engine:

To manage the four distinct material flows, we needed a robust mechanism to generate transport tasks the moment a material was ready. We achieved this using a combination of JaamSim’s powerful objects:

• Four Assign Entities: Each material type was mapped to a dedicated Assign entity. When a unit of material is ready for transport, this object instantly generates a "task" entity. It then stamps this task with critical data, such as Material_ID and Pickup_Point, creating a clear and structured request for the system.

• ExpressionThreshold Triggers: These objects function as the system's "eyes." They constantly monitor the status of each material line. The moment a material is ready for pickup (e.g., a pallet is full), the ExpressionThreshold activates the corresponding Assign entity, officially launching the transport task.

2. The Real-Time Monitoring Pulse:

An automated system must be incredibly responsive. To simulate this, we implemented an EntityGenerator configured to produce a high-frequency digital "pulse" (e.g., every 100 milliseconds). This pulse continuously scans the task triggers. The instant a new task is generated, this monitoring logic detects it and prepares it for assignment.

3. The External Brain: Python-Powered Fleet Management:

This is where our custom logic truly comes to life. Once the monitoring pulse detects a new task, it doesn't just randomly assign it. Instead, it securely passes the task information (Material_ID, Pickup_Point) to an external unit running a Python script.

This Python script acts as the centralized brain for the AGV fleet. Its functions are to:

• Receive the new task details from the JaamSim model.

• Analyze the real-time state of the entire AGV fleet, including each vehicle's precise location, status (idle, charging, busy), and battery level.

• Calculate the most efficient assignment by identifying the nearest and available AGV.

• Dispatch the command, assigning the task to the selected AGV and sending the instructions back to the JaamSim model to be executed.

This hybrid approach—using JaamSim for the physical simulation and Python for the advanced control logic—allows us to build and test a highly realistic and intelligent RCS that is tailored to the client's specific needs.

Why This Detailed Simulation Matters for Your Business

This process is fundamental to how we deliver value and ensure project success:

• De-Risking Your Investment: By building and validating the entire operational flow in a digital twin, we identify potential bottlenecks, optimize the required number of AGVs, and prove the system’s design before a single piece of hardware is ordered.

• Truly Custom Solutions: Your factory isn't generic, and your automation shouldn't be either. This methodology allows us to develop control logic that matches the specific rhythm and priorities of your operation.

• Building Confidence Through Proof: We don’t make assumptions; we model, test, and verify. This rigorous, data-driven approach gives our clients the confidence that the solution we design will perform as expected from day one.

By investing in thorough simulation upfront, we ensure your automation project is not just a capital expenditure, but a strategic success.

Curious about how a custom simulation could optimize your next automation project? Contact Linebridge Solutions today to start the conversation.