Proposal for NVIDIA Jetson Orin Nano Sponsorship
Project Title: Development of an Autonomous Surveillance and Combat-Ready Drone Using Edge AI
Submitted by:
Udegbe Chidiebere
Final-Year Mechatronics Engineering Student
Afe Babalola University, Ado-Ekiti, Nigeria
Contact: +234-8116085405
Email: audegbe2003@gmail.com
Date: October 05, 2025
Project Overview
As a passionate mechatronics engineering student entering my final year, I am ambitious about pushing the boundaries of robotics and AI in unmanned aerial vehicles (UAVs). My capstone project aims to design and build a next-generation autonomous drone optimized for surveillance and combat-ready applications. This drone will leverage real-time AI to achieve full automation, including obstacle avoidance, threat detection, and adaptive navigation in dynamic environments.
The project draws inspiration from advanced systems like Tesla’s Full Self-Driving technology but adapted for aerial drones, similar to those developed by companies like DJI and Skydio. By integrating edge computing, the drone will process sensor data locally, ensuring low-latency responses critical for real-world scenarios such as security monitoring or tactical operations.
Objectives
- Achieve Autonomous Flight: Enable the drone to navigate predefined waypoints using GPS fusion while dynamically rerouting around obstacles detected via computer vision.
- Implement Real-Time Threat Detection: Use AI models (e.g., YOLO for object recognition) to identify potential threats or targets in live video feeds.
- Ensure Combat-Readiness: Incorporate modular payloads for simulation of non-lethal actions (e.g., marking or signaling), with emphasis on ethical AI use in surveillance.
- Demonstrate Edge AI Efficiency: Run all computations onboard to minimize reliance on cloud services, highlighting the capabilities of compact hardware in resource-constrained settings.
Technical Specifications
- Hardware Components:
- Onboard Computer: NVIDIA Jetson Orin Nano (requested sponsorship) – Chosen for its up to 200 TOPS of AI performance, enabling efficient inference of neural networks like CNNs for vision tasks.
- Sensors: Intel RealSense D455 depth camera for 3D mapping and obstacle detection; optional ultrasonic sensors for redundancy.
- Frame and Propulsion: Custom 5-inch carbon fiber quadcopter frame with brushless motors for stability and endurance.
- Power: 6000mAh LiPo battery for extended flight times (target: 20-30 minutes).
- Software Stack:
- Operating System: JetPack SDK on Ubuntu Linux.
- Frameworks: ROS2 (Robot Operating System) for sensor fusion and path planning; TensorFlow Lite or PyTorch for lightweight AI models.
- Algorithms: SLAM (Simultaneous Localization and Mapping) for environment mapping; A* or DWA for path planning; Computer vision libraries like OpenCV for image processing.
Timeline
- September 2025 – October 2025: Research, design, and hardware acquisition (including Jetson Orin Nano integration).
- November 2025 – February 2026: Software development, AI model training on simulated data, and initial prototyping.
- March 2026 – May 2026: Testing, iteration, and optimization (indoor/outdoor flights, edge-case simulations).
- June 2026: Final demonstration, documentation, and project defense at Afe Babalola University showcase.
Why NVIDIA Jetson Orin Nano?
The Jetson Orin Nano is ideal for this project due to its compact size, low power consumption, and superior AI acceleration compared to budget alternatives like Raspberry Pi 5. It allows real-time processing of high-resolution sensor data (e.g., 720p video at 60 FPS) without offloading to external servers, which is essential for autonomous operations in remote or jammed environments. Without this hardware, the project would be limited to basic functionalities, reducing its innovation and impact.
Budget and Sponsorship Request
As a student with limited financial resources, I cannot afford the Jetson Orin Nano at this time (retail price: ~$500). If sponsored, I commit to:
- Documenting the entire build process in a video series (“Powered by NVIDIA Jetson”) on YouTube and social media.
- Sharing open-source code snippets and project insights on GitHub.
- Crediting NVIDIA prominently in my final report, university presentation, and any publications.
- Providing updates and feedback on the hardware’s performance in a real student project.
This sponsorship would not only enable the project’s success but also showcase NVIDIA’s technology in an emerging market like Nigeria, inspiring other young engineers.
Expected Outcomes and Impact
- A functional prototype demonstrating AI-driven autonomy in drones.
- Contributions to open-source robotics communities.
- Potential for real-world applications in security, agriculture monitoring, or disaster response.
- Personal growth toward a career in AI and robotics, with NVIDIA’s support as a key enabler.
Thank you for considering this proposal. I am eager to collaborate and make this project a testament to NVIDIA’s innovation.
Sincerely,
Udegbe
Mechatronics Engineering, Afe Babalola University