Automated Guided Vehicle for Material Transportation – A team of mechanical engineering students designed and built an autonomous guided cart to transport materials around a manufacturing facility or warehouse. The cart used sensors like ultrasonic sensors, infrared sensors and cameras along with onboard computers and software to navigate predetermined paths and avoid obstacles. It could detect loading dock locations, load/unload materials automatically and navigate to the desired destination on its own. This project demonstrated skills in mechanical design, embedded systems, programming and autonomous systems.
Smart Irrigation System Using IoT – For their capstone, a group of electronics and communication engineering students developed an IoT-based smart irrigation system for agricultural fields. It consisted of soil moisture sensors installed in the field that could periodically detect the moisture levels. This sensor data was sent wirelessly to a central server using LoRaWAN technology. The server analyzed the data using machine learning algorithms to determine which parts of the field needed water and sent wireless commands to automated valves to control the water flow accordingly. It helped optimize water usage and reduce manual labor. This project tested the students’ abilities in IoT, embedded systems, cloud computing and machine learning.
Wireless Brain Computer Interface – A biomedical engineering capstone group developed a non-invasive brain computer interface that could recognize different thoughts using EEG readings and trigger corresponding actions. They used a affordable and portable EEG headset to record brain wave patterns. Custom machine learning models were trained on these EEG datasets to classify thoughts like ‘left’ or ‘right’. When the model predicted a thought with high confidence, it sent a wireless signal to move a robotic arm in that direction. This helped people with mobility issues communicate and interact digitally using just their brain. The students gained practical experience in biomedical instrumentation, ML modeling, wireless communication and assistive technologies.
Mobile App for Structural Analysis of Bridges – As part of their civil engineering capstone, a team designed and developed a comprehensive mobile application for structural analysis and condition assessment of bridges in the field. Civil engineers could use the app to capture images and videos of bridges during inspections. Advanced computer vision and image processing algorithms within the app could automatically detect damage, measure cracks and corrosion. It also provided analytical tools and pre-programmed calculations to assess the structural integrity and remaining life of bridges. All inspection data was uploaded to a cloud server for further review. This project allowed students to apply their learning in areas like structural analysis, computer vision, cloud technologies and mobile development.
Car Racing Robot – For their final year mechanical engineering project, a group of students took on the challenging task of building an autonomous racing robot from scratch. They designed a lightweight but robust chassis using CAD tools and 3D printing. Mechanisms were added for steering, traction and maneuvering over uneven off-road terrains at high speeds. Onboard sensors, microcontrollers and deep learning models were integrated to enable self-driving capabilities without any remote control. The robot could perceive its surroundings, detect and avoid obstacles on the race track using computer vision. It could also strategize optimal paths for navigation and overtaking other competitor bots during races. Through this project, the students enhanced their expertise in various mechanical, electrical and software skills crucial for robotics projects.
Smart Home Automation using Raspberry Pi – An interdisciplinary team of Computer Science, Electronics and Electrical Engineering students came together for their capstone to build a smart home automation prototype. They installed various smart devices like automated lights, security cameras, smart plugs and IR sensors in a practice home setup. These were connected wirelessly to a Raspberry Pi single board computer acting as the central hub and server. Custom home automation software was developed to integrate these IoT devices and enable remote monitoring and control via a user-friendly mobile app interface. Users could control appliances, get alerts, watch live feeds and automate scenarios like ‘Away mode’. The project allowed students to gain applied experience in IoT, embedded systems, cloud computing, network protocols and full stack mobile development.
All these examples demonstrate innovative and interdisciplinary capstone projects across different engineering domains that equip students with practical, hands-on skills to solve real world problems. Through self-directed project execution spanning months, students strengthen their technical abilities while also developing valuable soft skills in teamwork, project management, communication and presentation. Well planned capstone experience near the end of undergraduate studies helps prepare engineering graduates to hit the ground running in their future careers.