How to Approach Robotic Packing and Drone Navigation Assignments Using MATLAB and Simulink
Between January and March 2021, two student teams from a leading UK university embraced the challenge of designing intelligent, autonomous systems using MATLAB, Simulink, and Simscape. What made their work truly remarkable was that most students had little prior experience with these tools. Yet, within just seven weeks, they developed complete working simulations that addressed real-world engineering challenges. These projects not only tested their technical skills but also demonstrated how MATLAB-based environments can be powerful platforms for system modeling, control design, and intelligent automation.
The two standout projects included the creation of an automated robotic packing system and a drone-based delivery simulation. These simulations went beyond just modeling mechanical systems—they involved applying logical thinking, optimizing processes, creating user-friendly interfaces, and exercising sound engineering judgment. Students had to approach each task with precision and innovation, making critical decisions to achieve functionality in a simulated setting. Their work shows how even with limited experience, complex systems can be developed using the right tools and mindset. If you’re working on similar projects or need to solve your robotic assignment using MATLAB and Simulink, these examples provide excellent insight into what’s possible within a short time frame using virtual prototyping and automation design.
Project 1 – Automated Packing Robot Using MATLAB
The first project tackled a real-world logistics challenge, how to efficiently pack 3D printed items with irregular shapes into shipping boxes. This isn't just a common warehouse issue; it's a complex engineering problem that involves object recognition, spatial optimization, and robotic control.
System Breakdown
To manage complexity, the students broke the system into four interconnected subsystems: sensing, planning, control, and actuation. Simulated sensors provided real-time object dimensions and positions, which fed into a planning algorithm that decided how best to place items inside a shipping box. Once the ideal spot was chosen, control logic determined how the robot should move, and the Simscape-based actuators translated those commands into physical motion in the simulation. This approach allowed for modularity and clearer debugging, especially important in fast-paced academic projects.
Simulating the Environment
Using Simscape Multibody and Simulink, the students recreated a functional warehouse-like environment. It included conveyor belts, shipping boxes, and a delta robot capable of pick-and-place operations. The delta robot's unique structure, with three parallel arms and a vacuum head, was especially suited for repetitive packing tasks. Contact forces were modeled to simulate item interactions with surfaces, adding realism to the virtual system. The environment was also outfitted with simulated sensors for collecting movement and positioning data to inform the robot’s actions.
Vision System Using Simscape
Rather than using complex image processing or machine vision algorithms, the team used Simscape’s Transform Sensors and PS-Simulink blocks to simulate a vision system. These sensors gathered data about distances and angles between the robot and objects in its environment. By feeding this information through MATLAB functions, the system could decide where and how to place each item. This efficient sensing strategy allowed for sufficient realism without overcomplicating the project, helping the team focus on control and packing logic.
Process Optimization with MATLAB
The real challenge came with optimizing how the robot packed the items. The team used MATLAB’s powerful matrix computation capabilities to build a packing optimizer that evaluated available space inside a box and suggested the best positions for new objects. By projecting 2D slices of the current contents and comparing them to the dimensions of the incoming object, the system could make intelligent decisions about object placement. This process involved creating a 3D spatial representation of box contents and applying heuristic-based logic to find the next best spot.
Path Planning and Control
Once the item’s ideal location was determined, the robotic arm needed to follow a precise path to place it. The students used MATLAB to handle decision-making logic and Simulink for modeling robot motion and torque application. This clear division of tasks between tools helped them design smooth transitions and maintain accuracy. The system calculated movement trajectories and then used joint torque calculations to move the robot arm accordingly. MATLAB’s scripting and Simulink’s block-based control modeling created a powerful combination for implementing motion control.
Simulation Challenges
Simulation speed and stability became major concerns during development. Modeling contact between objects often led to small oscillations in the robot’s movement, which caused the simulation to slow down significantly. To tackle this, the team adjusted the solver step sizes and imposed motion limits on the robot to avoid unstable interactions. They also refined the geometry of the environment to minimize simulation artifacts. This trial-and-error process mirrored real-world engineering, where trade-offs are often necessary to balance accuracy and performance.
Project Outcome
By the end of the project, the students had created a fully functioning simulation of an autonomous robot that could scan, plan, and pack diverse items with high efficiency. It was a complete logistics automation solution modeled entirely within MATLAB and Simulink. Through this hands-on experience, the students not only mastered simulation and control concepts but also learned the value of modular design and real-time system feedback.
Project 2 – Drone-Based Delivery Simulation
The second team set out to design a drone delivery system for packages within a university campus. Unlike typical drone simulations that focus on flying physics alone, this project integrated geographic data, scheduling logic, real-time control, and user interface design. The result was a robust, multi-component simulation that reflected the complexity of real-world autonomous delivery systems.
Project Setup
The simulation environment was modeled after the actual university campus. Students used Blender to generate 3D models of buildings and terrain, which were then imported into Unreal Engine for a realistic virtual landscape. At the core of the system was a MATLAB App Designer interface that allowed users to create, schedule, and manage delivery missions. The system also included routing algorithms, real-time state tracking, and Simulink models for drone behavior—all connected through a modular framework.
Custom UI in MATLAB
The App Designer interface served as the mission control center. Users could select delivery locations from a map, enter specific coordinates, and track drone progress in real time. The state transitions and mission logic were implemented in Simulink using Stateflow, while callback functions in MATLAB ensured that data updates and interface events were handled smoothly. This tight coupling of user interface and control logic helped make the simulation interactive and intuitive, even for first-time users.
Environment Modeling
Creating a realistic campus model was a key part of the project. Using GIS data and Blender’s Python scripting, the team built 3D meshes of the university's West Cambridge site. This included buildings, trees, and terrain elevation, which were rendered in Unreal Engine 4 and linked with Simulink through the Scenario Block. This setup enabled high-fidelity testing of the drone’s navigation paths and provided a clear visual for debugging and presentation.
Intelligent Routing Algorithm
Pathfinding was essential for navigating the campus. The students chose the RRT* (Rapidly-exploring Random Tree Star) algorithm for its balance between speed and path quality. Compared to A*, which often yields the most efficient path but at a high computational cost, RRT* offered fast responses suitable for interactive simulations. Blender’s ray-casting functionality helped generate 3D occupancy maps of the environment, which the routing algorithm used to avoid buildings and obstacles during flight.
Smart Scheduling Logic
Managing multiple delivery missions required smart scheduling. The team initially explored external libraries but ultimately implemented a custom scheduling system in C++ integrated with MATLAB using MEX files. The scheduler used pre-computed route distances and tested different permutations to find the most efficient delivery order. This saved time and allowed seamless integration with the UI, enabling dynamic mission updates and minimal delay between consecutive deliveries.
Drone Communication via MAVLink
For drone-ground station communication, the students implemented the MAVLink protocol, a common standard in the drone industry. Commands were sent from the MATLAB interface to the drone simulation, which then adjusted flight parameters using PID controllers. Each time the drone completed a mission, it communicated back to the ground station to trigger the next task. This closed feedback loop allowed the system to manage multiple missions autonomously.
Testing and Validation
Given the complexity of the system, thorough testing was essential. The team used MATLAB’s unit testing tools to validate algorithms, ensure UI responsiveness, and confirm that mission transitions worked correctly under different conditions. This structured testing approach improved the system’s robustness and reduced bugs, making the final simulation more reliable and closer to real-world expectations.
Why Simulink?
Throughout both projects, Simulink played a pivotal role. Its ability to model real-time control systems using drag-and-drop components helped the teams design and iterate quickly. Simulink’s integration with Unreal Engine, support for physics-based simulation through Simscape, and real-time data inspection made it a powerful platform for multidisciplinary student projects. From drone flight paths to robotic arms, Simulink enabled rapid prototyping and testing without risking real-world equipment.
Final Thoughts
These two projects exemplify how MATLAB, Simulink, and Simscape can transform ambitious ideas into sophisticated simulations. The student teams moved from problem statements to working systems—designing, testing, and refining intelligent autonomous solutions. Through these projects, they gained hands-on experience in robotics, optimization, control theory, and interface development.
For students aiming to specialize in automation, robotics, or system modeling, such projects offer a valuable opportunity to apply theoretical knowledge in a practical, simulated environment. Whether it's a robot packing boxes or a drone navigating a campus, MATLAB and Simulink provide the perfect launchpad for innovation in engineering education.