Multimodal UAV Collision Avoidance Detection & Tracking
If you have excellent programming skills and some knowledge of machine learning consider this exciting project !
Project description
In recent decades, the world has seen an unprecedented growth and improvement of unmanned aerial vehicle technologies ( UAV, also called drones) with a wide range of applications in transportation, search and rescue, security surveillance, communication relays, filming, entertainment, and monitoring. The existing methods are not designed to effectively handle security and privacy related issues or a wide range of weather conditions.
Project outcome
This project aims to develop a novel, multi-modal obstacle sense and tracking system.
Learning outcomes
The specific outcomes of this project will be identified by the faculty mentor at the beginning of your collaboration.
Project details
| Length of commitment | Longer than a semester; 6-9 months |
| Start time | Summer, Fall |
| In-person, remote, or hybrid? | Hybrid |
| Level of collaboration | Small group project (2-3 students) |
| Benefits | Potential academic credit (through independent study or supervised research requirement) |
| Who is eligible | Junior and senior undergraduate students with prerequisite skills & courses in Python, Intro to ML/Intro to AI, or related courses. |
Project mentor
Sreyasee Das Bhattacharjee
Assistant Professor of Research & Training
Computer Science & Engineering
Phone: (716) 645-4769
Email: sreyasee@buffalo.edu
Start the project
- Email the project mentor using the contact information above to express your interest and get approval to work on the project. (Here are helpful tips on how to contact a project mentor.)
- After you receive approval from the mentor to start this project, click the button to start the digital badge. (Learn more about ELN's digital badge options.)
Preparation activities
The specific preparation activities for this project will be customized through discussions between you and your project mentor. Please be sure to ask them for the instructions to complete the required preparation activities.
Keywords
computer science, engineering, unmanned aerial vehicle, UAV, programming, machine learning