How to Tackel Robotics Assignments Using MATLAB and Simulink

David Hensley

Australia

Simulink

David Hensley has over 12 years of experience in robotics system design and academic mentoring. He earned his Master’s degree in Electrical Engineering from Western Sydney University, Australia.

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Robotics Competitions and Their Significance

The French Robotics Cup and Eurobot gather hundreds of student teams every year around a central theme. Each year the theme changes, and in 2019, it was based on the periodic table’s 150th anniversary, called “Atom Factory.”

In the French Robotics Cup, more than 160 teams participated, most of them university students, but there were also high school teams and even professional engineers. Similarly, Eurobot attracts participants worldwide, with teams coming from Canada, Taiwan, and across Europe to compete against the winners of the French event.

The format of the competition is straightforward but demanding. Each match takes place on a 2-by-3 meter table, where up to four autonomous robots (two per team) compete to earn points by moving pucks and performing specific tasks. A match lasts 100 seconds, and during this short time frame, robots must navigate, avoid collisions, and execute their programmed strategies.

This setup mirrors the challenges students face in robotics assignments – limited time, resource constraints, and the need for optimized strategies. For many students, seeking help with MATLAB assignment becomes essential in understanding how to design algorithms, test simulations, and apply control techniques effectively in both coursework and competitions. These competitions act as a large-scale laboratory where students apply theories they have studied in their courses.

Challenges Students Face in Robotics Assignments

Building an autonomous robot is no simple task. Students must solve numerous problems simultaneously, ranging from mechanical design to software development. Here are some of the core challenges:

1. Highly Integrated Systems

Robots typically rely on an embedded computer such as a Raspberry Pi or LattePanda, paired with microcontrollers like Arduino or STM32. These devices must communicate through protocols like I2C, Ethernet, or CAN. In many cases, robots also use additional sensors such as cameras or lidar to understand their environment.

The integration of all these systems is challenging. For students, designing an algorithm that can run reliably across different platforms requires both hardware knowledge and software expertise. MATLAB and Simulink provide a streamlined approach to test algorithms in simulation first and then automatically deploy them to real hardware, saving significant time and effort in assignments and projects.

2. Navigation and Control

When multiple robots move at speeds of up to 5 meters per second on a small playing field, navigation becomes critical. Robots must avoid each other, obstacles, and walls while reaching their targets.

To achieve this, students often rely on a combination of wheel odometry and range-finding sensors such as sonar or lidar. These sensors require sensor fusion techniques to improve accuracy. For instance, a probabilistic roadmap can be implemented in MATLAB to test navigation before applying it to real robots.

Assignments on navigation usually focus on path planning, localization, and obstacle avoidance. MATLAB provides built-in functions to handle these challenges, making it easier for students to visualize and refine their strategies.

3. Manipulator Control

Many robots use manipulators such as robotic arms or suction grippers to pick up and place objects. Controlling these manipulators requires solving inverse kinematics problems to position the gripper accurately.

Assignments often ask students to design motion trajectories, simulate manipulator control, and validate performance under varying conditions. Simulink simplifies this process by providing tools to model robotic arms and generate motion profiles, making the assignment both manageable and insightful.

4. Low-Level Actuator Control

Whether using electric motors, pneumatics, or hydraulic actuators, students need precise low-level control to execute tasks. Developing a mathematical model of actuators, either from datasheets or experimental data, is a common assignment requirement.

Using MATLAB, students can design and test PID controllers, optimize parameters, and then implement them in real hardware. This workflow mirrors industrial practices, preparing students for future engineering challenges.

Computer Vision in Robotics Assignments

In recent years, robotics competitions have increasingly emphasized computer vision. Robots must identify objects, track colors, and even recognize patterns in their environment.

Assignments related to computer vision often include tasks such as:

• Implementing color thresholding and blob detection.
• Designing deep learning models to classify objects.
• Deploying image-processing algorithms on embedded platforms like Raspberry Pi.

MATLAB supports both classic image-processing techniques and advanced deep learning applications. Students can prototype on a laptop with GPU acceleration and then deploy models on embedded hardware, aligning perfectly with competition requirements.

Strategy and Intelligence

Beyond sensors and actuators, the real challenge lies in giving robots “intelligence.” A robot must decide what action to take when unexpected events occur. Should it change its path? Should it prioritize collecting a nearby puck or avoid a collision?

Assignments in this area often involve designing finite state machines or dynamic path-planning algorithms. MATLAB’s Stateflow tool allows students to model robot strategies as state machines and generate standalone code to run on embedded systems.

Closed-loop strategies, where robots adapt to real-time changes, are often the key to success in competitions. Assignments focusing on strategy help students understand how to design robust systems that perform reliably under uncertainty.

Project-Based Learning and Skill Development

Robotics competitions exemplify project-based learning, where students acquire knowledge by working on practical projects rather than just theoretical exercises. These projects demand a combination of technical and soft skills.

Cross-Technical Skills

Students need to integrate knowledge from multiple domains—mechanical, electrical, and software engineering. For example, a software developer must understand how a motor responds to commands, while an electronics student must know how sensor data is interpreted in software.

Soft Skills

Assignments and competitions also build non-technical skills such as teamwork, communication, and project management. Teams must secure sponsorships, prepare project posters, and present their work, simulating real-world professional environments.

How MATLAB and Simulink Support Students

MATLAB and Simulink are invaluable for robotics assignments because they reduce the complexity of moving from theory to practice. Some key benefits include:

• Simulation before implementation: Students can test algorithms in a simulated environment before deploying them to hardware, reducing costly errors.
• Hardware compatibility: MATLAB supports a wide range of microcontrollers and embedded boards, allowing direct code deployment.
• Toolboxes for every need: From Robotics System Toolbox to Computer Vision Toolbox, students have access to specialized functions for complex tasks.
• Integration with ROS: Many competitions and research projects now rely on the Robot Operating System (ROS). MATLAB provides built-in integration, allowing students to prepare for real-world robotics applications.
• Learning resources: MATLAB and Simulink offer tutorials, documentation, and video series like the Robotics Arena, which guide students through fundamental and advanced topics.

Lessons for University Assignments

The challenges faced in competitions mirror those encountered in university robotics assignments. Here’s how:

• Navigation and control assignments: Competitions show the importance of combining odometry with sensor fusion, a skill directly applicable in coursework.
• Manipulator control assignments: Designing pick-and-place systems highlights the use of inverse kinematics and motion trajectory planning.
• Computer vision assignments: Real-world robot vision tasks prepare students for coursework on image processing and deep learning.
• System integration assignments: Working with multiple hardware and software components reflects the complexity of multidisciplinary projects in academia.

By participating in robotics competitions or studying them, students gain inspiration and practical insights that can help them complete university assignments more effectively.

Final Thoughts

Robotics competitions like the French Robotics Cup and Eurobot are more than just contests; they are real-world laboratories where students apply theory to practice. They mirror the challenges of robotics assignments—system integration, control, navigation, computer vision, and strategy.

MATLAB and Simulink stand out as essential tools, providing students with the ability to simulate, test, and deploy algorithms across multiple domains. For students working on robotics assignments, these tools not only make the process more efficient but also give them a professional edge.

In an academic environment where project-based learning is becoming increasingly important, MATLAB and Simulink ensure that students are prepared for the future—whether in competitions, assignments, or professional careers.