How to Built Autonomous Systems Using MATLAB and Simulink
The true strength of MATLAB and Simulink is revealed when students apply them to solve complex, real-world engineering problems. One such example comes from a recent group design project where two university teams developed innovative systems using MATLAB, Simulink, Simscape Multibody, and Unreal Engine. One team created a fully simulated robotic packing system, while the other designed an autonomous drone delivery platform. What made this even more impressive was that most students involved had little prior experience with MathWorks tools. Despite this, they built functional, industry-level simulations by leveraging the modeling, control design, and visualization features of these platforms.
Throughout their journey, the students encountered various technical challenges—from system modeling to real-time simulation—but overcame them with structured planning and a strong grasp of engineering principles. By deeply integrating Simulink and its toolboxes, they were able to break down complex tasks into manageable blocks and simulate realistic behavior. These kinds of projects not only improve technical skills but also build confidence in handling industrial-scale tools. For students looking for help with Simulink assignment tasks or struggling to understand how to approach such simulations, these examples highlight how guided practice and real application can make a huge difference in learning and performance.
Building a MATLAB Robot
One of the projects tackled a practical challenge faced by 3D printing companies—efficiently packing items of various shapes into boxes. The students created a robotic system simulation using MATLAB and Simulink to automate this packing process. Their design followed four key stages: sensing, planning, controlling, and actuating. First, the robot scanned objects moving on a conveyor belt, gathering data about their positions and sizes. This information was fed into a process optimizer that determined the best way to arrange items inside the shipping box. Then, the control module calculated the precise movements needed for the robotic arm to pick and place each item. To make the simulation realistic, Simscape Multibody was used to model conveyors, boxes, and the robot’s mechanics. Projects like this highlight the complex applications of MATLAB, and students often seek assistance with MATLAB assignment to handle such sophisticated simulations effectively.
How the Delta Robot Was Built and Simulated
The core mechanical design chosen was a delta robot, ideal for “pick and place” operations due to its precision and mechanical simplicity. The robot consists of a base with three arms connected to a head, which always remains parallel to the base while moving in three-dimensional space. To simulate item manipulation, the head was fitted with a virtual vacuum that could pick up and place objects. This design made sense for simulation since the robot didn’t need to rotate items—only lift and position them. In the simulated setup, two conveyor belts were also controlled by the system—one for incoming items and another for receiving packed boxes. This cohesive structure allowed the entire process to be automated and monitored through Simulink.
Image Processing Handled in Simulink
To sense objects and their spatial orientation, the students used Transform Sensors in Simscape Multibody, which allowed for the measurement of position and rotation between reference frames. These readings were passed to Simulink using PS-Simulink blocks and processed further with MATLAB Function Blocks. In the simulation model, each object in the system, including the robot, boxes, and items, was assigned sensors to constantly track movement and interaction. This setup ensured accurate data transfer to the control and planning systems, providing real-time updates on object positions for decision-making.
Process Optimizer
Optimizing the placement of objects inside a box is a complex problem, especially when dealing with items of irregular shapes and varying sizes. The students tackled this by reducing the 3D problem to a series of 2D slices, essentially computing the shadow cast by items at different heights. These 2D projections were used to reconstruct the available empty space in 3D, allowing the optimizer to determine viable locations for the next item. The system preferred positions at the lowest possible height and, in case of multiple options, selected ones closest to the corners—a heuristic known to result in tighter packing. This method not only improved efficiency but also mimicked the kind of algorithms used in commercial warehouse systems.
Planning and Control
The planning and control module was the engine that drove the robot’s motion. It received input from the image processor and the process optimizer to calculate how the robot should move. Planning involved creating a path from the robot’s current position to the target placement, while control focused on generating precise torque values for the motors to move the arms along the path. MATLAB and Simulink were crucial here, particularly for defining motion profiles and implementing PID-based torque controllers. Achieving smooth, stable motion required extensive tuning and iteration, something that the MATLAB control toolboxes made significantly more manageable.
Simulation Challenges Were Overcome
Despite their success, the team ran into major performance issues during simulation. The primary bottleneck was the slow simulation of contact forces between items and the shipping box. These forces caused micro-oscillations that drastically slowed down the system—sometimes up to 500 times slower than real-time. To address this, they adjusted solver settings by increasing the step size limit, which improved speed but introduced numerical instability. The robot began exhibiting erratic motion, often spinning wildly due to unstable forces. Eventually, the team fine-tuned their movement paths and solver configurations to achieve a balance between speed and stability. This process highlighted the importance of simulation optimization and understanding solver dynamics in MATLAB.
West Cambridge Air Freight
In a completely different but equally ambitious project, another student team developed a simulated drone delivery system designed to transport lightweight packages across a large academic campus. The system included a custom-built user interface, a 3D campus model, real-time route planning, delivery scheduling, and communication via the MAVLink protocol. The entire simulation was built around a real-world use case of coordinating autonomous drones to deliver items between buildings safely and efficiently.
User Interface
The interface was developed using MATLAB’s App Designer, which allowed the team to build a professional, interactive control panel. Users could select predefined pickup and drop-off points or enter custom coordinates using geoaxes, which provided real-time visualization of the drone’s movement. Callback functions were used to handle mission events, such as the drone landing or receiving a new route, and all logic was tied together using StateFlow blocks in Simulink. The interface also connected to a compiled MEX version of the scheduling algorithm, ensuring fast communication between components and smooth mission execution.
West Cambridge Model
To simulate the actual environment, the students created a 3D model of the campus using Blender. GIS data was imported to get accurate ground textures, elevation, and building layouts. The model was textured and baked onto a UV map, which was exported to Unreal Engine. This Unreal model was then embedded into Simulink’s scenario block, giving the drone simulation a photorealistic environment. Integrating Blender, Unreal, and Simulink was technically challenging, but it added an incredible level of realism to the system.
Routing Algorithm
For path planning, the students selected the RRT* algorithm. While A* provides optimal routing, it was too slow for real-time missions. RRT* offered faster calculations with minimal compromise in path quality. The drone was programmed to navigate around buildings rather than fly directly over them, which better simulated urban delivery scenarios. The team also built an occupancy map using Blender’s Python API. They extracted building vertices and used ray-casting to determine ground elevations and obstacles—data that was then used by the routing algorithm for pathfinding.
Scheduling Algorithm
Mission scheduling was another key part of the project. The students explored libraries like Google’s OR-Tools but decided to write a custom scheduling algorithm in C++ for better integration and performance. The algorithm precomputed distances between all mission points and stored them in a cache. For every scheduling call, it randomly permuted the list of missions and selected the one with the lowest total travel distance. This was efficient enough for the project scale and could be scaled up for future use. The algorithm was wrapped in a MEX file for fast execution inside MATLAB.
MAVLink Interface
To simulate communication between the drone and the control station, the MAVLink protocol was used. At the beginning of each mission, a sequence of instructions including waypoints was sent to the drone. The drone then used PID controllers to navigate toward these points, adjusting its yaw, pitch, and roll as needed. Once the delivery was completed and the drone landed, a signal was sent back to the control system to initiate the next delivery route. This protocol-based setup closely mimicked real-world drone operations and added depth to the simulation.
Testing and Debugging with MATLAB’s Built-In Tools
Testing played a major role in both projects. The students used MATLAB’s unit testing framework to verify the correctness and efficiency of each component. For example, they were able to prove that A* was too slow and used this insight to switch to RRT*. Parameters for each algorithm were stress-tested under different conditions to find the optimal configuration. Similarly, the scheduling algorithm and UI were tested with multiple missions and varied input types to ensure robustness. This level of testing not only improved performance but also gave the teams a better understanding of integration challenges in large-scale systems.
Advantages of Using Simulink for Autonomous System Design
Simulink was central to both projects for a number of reasons. It allowed students to test their systems without risking physical hardware. The drag-and-drop interface of Simulink blocks enabled quick prototyping and testing. Integration with Unreal Engine provided an immersive visual environment. Tools like the Data Inspector helped debug wiring signals and track the drone’s behavior, while StateFlow blocks simplified logic design. Most importantly, it allowed for rapid iteration, enabling students to experiment, fail, and improve—all within a safe simulation space.
Conclusion
These two student-led projects are perfect examples of how MATLAB and Simulink empower users to go from concept to implementation with real-world complexity. Whether it was packing arbitrary-shaped objects with a delta robot or coordinating an autonomous drone fleet across a simulated campus, the students showcased technical depth, creativity, and problem-solving skills. More importantly, they demonstrated how accessible advanced simulation tools are for learners when guided properly. For any student looking to master MATLAB or build autonomous systems, these projects provide both inspiration and a blueprint. And for those seeking help with MATLAB assignments, there’s no better way to l