Capstone
The Senior Design Capstone Project is a collaborative program where engineering students gain hands-on design experience by developing innovative product prototypes. This project allows students to apply their theoretical knowledge to real-world engineering challenges, including sustainability and health and safety considerations.
If you’re interested in working with me, send me an email to discuss potential project topics.
Past projects
Spring 2024
Photo Album: link
RailVision: Overgrowth Detection Drone (3rd Place)
Team members: Osmin Nolasco, James Sullivan, Steeve Cantave, Dhruv Patel
Abstract: Every year, billions of dollars are spent towards railroad maintenance, with vegetation overgrowth maintenance being a prominent issue. Vegetation overgrowth creates fire hazards, infrastructure damage, and lead to many negative economic and environmental impacts. To address this problem, we built an autonomous VTOL drone using an energy efficient 4+1 quadplane configuration. Utilizing our image classifying CNN (RailNet), our quadplane autonomously flies alongside rail lines, detecting vegetation overgrowth in real time. Through RailVision, we aim to contribute to the safety of maintenance workers, address the inefficiencies of current overgrowth inspections, increase the lifespan of railroad infrastructure, and make leaps toward preventing wildfires to better conserve our environment.
Human Motion Estimation for Interactive Rehabilitation (8th Place)
Team members: Ronan John, Daniel Gameiro, Marco Garcia-Palma
Abstract: Sarcopenia is a musculoskeletal condition characterized by a loss of muscle and strength that affects a large portion of the elderly population globally. The condition can only be treated with physical therapy. However, not everyone who could benefit from PT is able to use it, either due to cost or location. The goal of our project is to provide a low-cost, at-home alternative to PT, using small integrated internal measurement units (IMUs) to track limb movements using inverse kinematics. This is paired with our software, which guides the user through different therapeutic exercises based on their goal, and gives them feedback on their performance. The results, although preliminary, indicate that this method can help patients reduce the effects of sarcopenia, and provide a way for progress to be tracked over time.
Spring 2023: The LanternPredator (The project ranked 6th out of 71 participants, making it into the top 10)
Abstract: Lanternflies are an invasive pest species that can cause significant economic damage to agriculture by affecting plants and crops and disrupting the balance of natural ecosystems. These insects have a fast reproduction cycle, can withstand high-temperature variations, and have no natural predators in the US, making it very difficult to control their spread. Motivated by this environmental issue, we designed the LanternPredator, an autonomous pest control robot to help control the population growth of lanternflies. The proposed solution integrates machine learning algorithms for detecting the right insect species, an acoustic stimulus to attract the insects to a zap trap, and sonar for autonomous navigation.
Team members: David Banyamin, Wei Gou, Mark Rezk, Wictor Fedorowiat
Advisor: Dr. Daniel Burbano Lombana
Fall 2022: Navigation Implementation of Industrial Vehicles
Abstract: Smart warehousing is the process of automating all the activities performed in a regular warehouse where raw materials and manufactured goods are stored. Smart warehousing has several advantages, from reducing operating costs and processing time to reducing human safety risks and optimizing physical storage space. The primary goal of our project is to develop an autonomous vehicle capable of navigating in a warehouse environment following a specific path. This research seeks to boost productivity in industrial settings by minimizing the wasteful movement of robotic vehicles, resulting in less time lost. This can open the door to a more general setting where a swarm of cooperative robots can fully automate the warehouse.