How to Built a PX4 Based Underwater Robot Assignment Using MATLAB
MATLAB has become an essential tool in robotics, enabling students, researchers, and professionals to develop smart and efficient robotic systems with greater ease. Its powerful simulation and control design capabilities allow users to model complex robots and test their performance virtually before building physical prototypes. One remarkable example comes from a student team that used MATLAB along with the PX4 autopilot platform to create an innovative underwater robot. This project stood out not only for its technical sophistication but also because it earned the team top honors at a major national competition in China.
The team combined MATLAB’s algorithm development environment with Simulink’s simulation tools to design and optimize the robot’s propulsion and control systems. Using MATLAB Coder, they generated embedded code that ran on the PX4 hardware, enabling real-time control of the underwater vehicle. This seamless workflow—from algorithm design and simulation to hardware implementation—showcases how MATLAB supports the entire robotics development process.
For students seeking help with robotics assignment, this project highlights the value of mastering MATLAB and Simulink for complex system design. Understanding these tools can significantly improve your ability to tackle robotics challenges and deliver innovative solutions like this underwater robot.
Understanding the Project Goal
The challenge was to design an advanced underwater robot capable of precise control and maneuverability. The project required a compact, high-performance propulsion system, robust control algorithms, and the ability to operate in three-dimensional underwater environments. Traditional underwater robots often use multiple fixed thrusters that can be bulky, inefficient, and complex to control in real-time. This team chose to move beyond conventional approaches and develop something far more sophisticated.
Their solution featured a vectorial propulsion system driven by three spherical magnetic coupling vector thrusters. With a smart control strategy and efficient code generation, the robot was brought to life and even tested successfully in real-world conditions.
Why Use Vector Thrusters?
Fixed thrusters are limited when it comes to achieving complex six-degree-of-freedom (6-DOF) movement, often leading to unnecessary parasitic thrust and limited maneuverability. Vector thrusters, on the other hand, provide more DOF, better thrust control, and greater efficiency. The trade-off, however, lies in the complexity of their control.
The main design challenge was deciding on the optimal layout of the three vector thrusters. The team needed to determine how to place and orient these thrusters to deliver the most effective and decoupled 6-DOF thrust possible. This would allow the robot to move freely and stably in all directions under water.
Algorithm Development Using MATLAB
To solve the thruster placement challenge, the team developed a genetic algorithm-based optimization method. This algorithm was used to find the best positions and angles for installing the vector thrusters. MATLAB played a central role in this phase, providing the tools needed to develop and simulate optimization routines with ease.
Thruster Layout Configuration
They began by formalizing the thruster configuration problem as an optimization task. The algorithm's objective was to identify a configuration that allows the maximum decoupled thrust in all six degrees of freedom. MATLAB’s optimization toolbox was utilized to simulate and test multiple configurations, eventually arriving at the optimal layout.
Control Allocation Through Convex Optimization
Once the layout was finalized, the next challenge was control allocation: how to distribute the required thrust force and torque among the three reconfigurable thrusters. This problem was formulated as a convex optimization task — minimizing error and energy consumption while respecting angle and force constraints of the thrusters.
They used the Lagrange method to solve this optimization problem in real-time, providing the best control allocation strategy for the underwater robot. MATLAB’s symbolic and numerical computation tools streamlined this process significantly.
Motion Control Strategies
For real-time motion control, a hybrid approach was adopted:
- On the horizontal plane, a backstepping control strategy was implemented. This approach, combined with Lyapunov stability theory, ensured robust trajectory tracking and system stability.
- On the vertical plane, a traditional PID controller was employed to manage depth control.
- Additionally, a Radial Basis Function (RBF) Neural Network was used to compensate for actuator saturation, enhancing robustness.
All these algorithms were developed, tested, and fine-tuned using MATLAB before deployment.
Simulation with Simulink
Simulation played a key role in the development phase. Using Simulink, the team created a complete dynamic model of the underwater robot. The model included three major components:
- Controller Module – for backstepping, PID, and RBF-based control.
- Control Allocation Module – for solving the convex optimization in real-time.
- Robot Dynamics Module – to simulate the physical response of the robot.
Simulink Model Design
Each block in the Simulink model was either built using standard Simulink library components or custom MATLAB functions. This modular structure made it easy to iterate on the design and perform thorough validation.
The robot dynamics were carefully modeled to include water resistance, thruster response, and buoyancy forces. This accurate simulation helped the team save both time and cost by minimizing the number of physical prototypes.
Trajectory Tracking Results
With all components in place, the simulation results confirmed that the control system could accurately track desired trajectories. The backstepping controller proved highly effective, and the robot demonstrated smooth, stable movement in the simulation environment.
Hardware Implementation with PX4 and MATLAB Coder
After successful simulation, the team transitioned to hardware development. They adopted a modular and bus-based architecture for the robot. At the heart of the system was a Holybro Pixhawk 4 — an open-source controller based on the FMUv5 PX4 flight stack.
Why PX4?
PX4 is widely used in the robotics community for its flexibility, community support, and hardware compatibility. Although it originated in aerial robotics, it has found wide adoption in ground and marine robotics as well.
Code Generation and Deployment
One of the major strengths of MATLAB is its integration with hardware. Using MATLAB Coder, the team automatically converted their control allocation algorithms into optimized C code. This code was then deployed directly to the PX4 controller with minimal manual adjustments.
This approach significantly reduced development time and ensured that the algorithm executed exactly as it had in the simulations, without translation errors.
Physical Experimentation
With everything integrated, the final stage involved testing the robot in a real-world environment. The team conducted several pool tests to validate its performance. During these trials, the robot was able to autonomously track a set trajectory while maintaining balance and orientation — a testament to the effectiveness of the control system.
Real-World Results
The experiment results aligned closely with the simulation data. The robot responded swiftly to trajectory commands and maintained stability even under varying conditions. These successful tests demonstrated the strength of the MATLAB-PX4 pipeline, from simulation to deployment.
Recognition and Presentation
Thanks to the excellence of their design, execution, and testing, the student team won not only the first prize in the MathWorks Awards but also the second overall prize in the national competition. Their work received recognition from academic and industrial experts alike.
The robot was later showcased at a national education expo, where attendees from universities and research institutes praised the innovation and technical quality of the project.
Future Opportunities with PX4 Support Package
For future development, the team plans to explore the UAV Toolbox Support Package for PX4 Autopilots. This package allows seamless access to PX4 peripherals from within MATLAB and Simulink, further simplifying embedded system design.
Combining this package with Embedded Coder will enable automatic generation of C/C++ code customized for various PX4-compatible flight management units (FMUs), such as Pixhawk and Pixracer. This model-based design workflow is already standard in many industries, and adopting it will further enhance the efficiency and flexibility of future projects.
What to Learn from This Assignment
This underwater robot project is a great example of how students can apply theoretical knowledge to practical challenges using industry-standard tools. Some valuable takeaways include:
- The Power of MATLAB and Simulink: These tools make it easy to simulate, analyze, and optimize complex control systems.
- Model-Based Design: Building and simulating models before deploying to hardware saves both time and cost.
- Integration with PX4: PX4 offers a versatile hardware platform, and when combined with automatic code generation, it enables rapid development cycles.
- Optimization in Robotics: Modern robotic systems benefit greatly from optimization techniques for control allocation and actuator placement.
Whether you're building drones, underwater vehicles, or ground robots, the techniques used in this project can help you manage complexity and build smarter, more efficient systems.
Final Thoughts
This underwater robot project clearly demonstrates what students can achieve when creativity is combined with powerful engineering tools. By effectively using MATLAB, Simulink, and PX4, the student team showcased their innovation and strong grasp of control systems, algorithm design, and real-time embedded implementation. Their work reflects the value of hands-on experience, where theory meets practical application in a seamless workflow.
One of the most impressive aspects of the project is how it brings together multiple disciplines — including control engineering, mechanical design, coding, and hardware integration. This kind of multidisciplinary learning is becoming essential for today’s engineering students and researchers. Projects like these not only test technical knowledge but also encourage collaboration and problem-solving under real-world constraints.
As MATLAB and its toolboxes continue to evolve, students have greater access to industry-level capabilities, making it easier to test, simulate, and deploy sophisticated robotic systems. For those who are beginning their own academic or research projects and need help with MATLAB assignment topics — especially involving simulations, control systems, or PX4 integration — this project serves as a great example and source of inspiration.
With the right tools, guidance, and effort, students can transform ideas into impactful innovations just like this award-winning underwater robot.