How MATLAB, Simulink, and ROS Work Together for Robotics Assignment

Dr. James Mitchell

USA

Simulink

Associate Professor of Robotics at UC Berkeley, USA. He specializes in control systems and ROS integration using MATLAB and Simulink, guiding students in applying theory to real-world robotics.

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The Robot Operating System, or ROS, is not really an “operating system” in the traditional sense. Instead, it’s a framework that provides a communication layer for distributed robotic systems. Robots today are rarely made of a single unit. Instead, they consist of multiple sensors, actuators, decision-making modules, and communication channels. ROS acts as the glue that holds these together.

Key reasons ROS has become a global standard:

• Distributed design: Different parts of the robot—like vision, control, navigation—can run independently while sharing data in real time.
• Community and libraries: A rich ecosystem of packages exists, covering everything from mapping and localization to motion planning.
• Industry adoption: ROS has moved from research labs into commercial products, including autonomous cars and drones.

For students, the relevance is clear: knowing ROS means you’re working with the same framework that real-world companies are using today.

MATLAB and Simulink in the Robotics Ecosystem

Before diving into the ROS connection, it’s important to recognize what MATLAB and Simulink bring to the table.

• MATLAB is a high-level environment where you can prototype algorithms quickly. It shines when handling matrix operations, data visualization, signal processing, or machine learning tasks.
• Simulink complements MATLAB with a graphical programming approach. Instead of writing lines of code, you connect functional blocks that represent components of a system. This makes it easier to build and simulate control systems or signal flows.
• Stateflow adds another layer, allowing you to model decision-making logic through state machines and flowcharts.

Taken together, these platforms allow you to build, test, and refine robotics algorithms in a controlled desktop environment before deploying them onto real robots.

The ROS Toolbox: Bridging MATLAB/Simulink with ROS

MathWorks introduced the Robotics System Toolbox in 2015, which later evolved into the ROS Toolbox in 2019. This toolbox is what makes MATLAB and Simulink ROS-aware. It enables three main workflows:

1. Desktop prototyping: Use MATLAB scripts or Simulink models to design and test algorithms while connected to a running ROS network.
2. Code generation: Convert algorithms into C++ ROS nodes that can run directly on the robot’s computer.
3. Hybrid workflows: Mix and match MATLAB prototyping with deployed ROS nodes for flexible experimentation.

The beauty of this setup is that you don’t have to abandon MATLAB or Simulink when your project involves ROS. Instead, these environments become part of the same workflow.

Connecting MATLAB to ROS

With ROS Toolbox, MATLAB can act as a ROS node itself. This means it can publish and subscribe to topics, call services, respond to actions, and interact with the ROS parameter server.

For students, the practical applications are immense. Imagine you’re working on:

• Sensor data processing: You can subscribe to topics streaming camera images, lidar scans, or point clouds and then analyze them in MATLAB.
• Control prototyping: You can write a control algorithm in MATLAB, test it live by publishing velocity commands to a robot, and see how it responds.
• Simulation integration: MATLAB can connect to simulation environments that run ROS, making it easier to validate algorithms without risking hardware damage.

The key idea here is that MATLAB provides an accessible, script-based way of interacting with ROS, making rapid prototyping possible even for complex robotic behaviors.

How Simulink Works with ROS

While MATLAB is text-based, Simulink takes a graphical approach. With the ROS block library, Simulink lets you model algorithms that interact directly with ROS topics and parameters.

Some benefits of Simulink in this context include:

• Visual programming: Algorithms are expressed through interconnected blocks, which is intuitive for control and signal processing tasks.
• Multirate design: You can model systems where different components operate at different rates, such as fast sensor sampling and slower decision-making loops.
• Hybrid modeling: MATLAB code, Simulink blocks, and Stateflow charts can all be combined, allowing different parts of your system to be represented in their most natural form.

For example, you could design a path-following algorithm where the vision system is modeled in MATLAB, the control loop is built in Simulink, and the decision-making logic is expressed in Stateflow. All of these can run within the same ROS-connected system.

From Desktop to Robot: Generating Standalone ROS Nodes

One challenge in robotics is moving from a desktop prototype to something that actually runs on a robot’s onboard computer. Installing MATLAB on a robot is usually impractical, especially for lightweight platforms like Raspberry Pi or embedded boards.

This is where code generation comes in. There are two main approaches:

1. Reusable C/C++ code: Using MATLAB Coder or Simulink Coder, you can generate C or C++ code from your prototypes. This code can then be integrated into your existing ROS system manually.
2. Direct ROS node generation: With ROS Toolbox, you can automatically generate full ROS nodes in C++. These nodes include not only your algorithm but also the necessary ROS communication interfaces.

The second approach is particularly powerful because it removes the manual translation step. It ensures that the behavior you tested in Simulink is faithfully replicated in the deployed ROS node.

Working with Generated Nodes

Once a node is generated, you have several ways to interact with it:

• Control from MATLAB: Start or stop nodes remotely, adjust parameters, or monitor performance.
• External mode: Keep the connection to Simulink open so you can tune parameters and visualize data while the code runs on the robot.
• ROS integration: Because the generated node is a standard ROS node, it can interact with all the other nodes in your system, including those written in C++ or Python.

This workflow blurs the line between prototyping and deployment. Instead of rewriting code, you’re essentially promoting your tested model into a deployable robot component.

Why This Matters for Students

For university students, especially those working on robotics projects, the MATLAB–Simulink–ROS workflow offers several educational and professional advantages:

• Learning industry-relevant skills: Companies working on autonomous driving, robotics, and drones actively use MATLAB, Simulink, and ROS.
• Bridging theory and practice: What you model in MATLAB or Simulink can directly run on a real robot, reducing the gap between classroom exercises and real-world systems.
• Reducing errors: Automatic code generation means fewer manual translation errors when moving from prototype to deployment.
• Scalability: Whether your project is a single robot in a lab or a distributed multi-robot system, the same workflow applies.

Challenges and Considerations

It’s worth noting that this integration is not without challenges:

• Resource overhead: Generated ROS nodes may be less lightweight than hand-optimized C++ implementations.
• Single-threaded execution in MATLAB: Subscriber callbacks and timers run in a single thread, which can limit concurrency.
• Dependency management: Setting up ROS environments and ensuring compatibility with MATLAB versions can sometimes be tricky.

For students, these challenges are part of the learning curve. They highlight the importance of understanding not just the tools but also the underlying system architecture.

Conclusion

MATLAB, Simulink, and ROS together create a powerful ecosystem for robotics research, prototyping, and deployment. Students working with these tools gain the ability to design algorithms in a familiar environment, test them against real or simulated data, and then deploy them directly onto robots without rewriting everything from scratch.

This workflow mirrors what happens in professional robotics development, making it highly valuable for academic projects and future careers alike.

At our team, we specialize in helping students master these workflows through assignments, projects, and guided explanations. Whether you’re learning how MATLAB connects to ROS, building multirate control systems in Simulink, or generating standalone nodes for a Raspberry Pi robot, the skills you build here are directly relevant to the robotics industry.