Stanford Chris Gregg’s Lab
Introduction
I was part of Chris Gregg's drone lab, which focuses on designing and building inexpensive flying drones for educational purposes. Our lab's goal is to create small, cost-effective drones that can be used as engaging robotic teaching tools in introductory Computer Science education. Specifically, I contributed to the printed circuit board (PCB) design and the computer-aided design (CAD) modeling of the drone chassis. Our project explored two viable drone types: quadcopters and helium-filled blimps. The primary deliverables included developing an Arduino-based drone platform suitable for mass production or assembly as a kit, creating introductory lessons for classroom use with drones, and conducting testing with real students at local schools.
Design
The project design phase involved extensive utilization of industry-standard tools and software. For the electronic circuit design, we employed KiCad, a powerful open-source software suite for creating schematics, performing electrical rule checks, and generating production-ready PCB layouts. The PCB designs were then fabricated using advanced printed circuit board manufacturing processes. Concurrently, the mechanical design of the drone chassis and components was carried out using Fusion 360, a comprehensive CAD software that enabled precise 3D modeling and simulation. We leveraged parametric modeling techniques and finite element analysis (FEA) to optimize the structural integrity of the drone design. Additionally, the project involved programming skills for integrating various sensors such as cameras, GPS, sonar, and LiDAR into the drone platform, enabling data acquisition and enhancing its functionality for educational purposes.
Skills
Expertise in PCB design using EDA tools like KiCad, including schematic capture, layout, and DFM/DFX verification
Knowledge of mechatronics, including mechanical design principles, actuator selection, and control system implementation
Familiarity with computer vision algorithms and techniques for image processing and object detection/tracking
Experience with sensor fusion and Kalman filtering for robust state estimation and navigation
Ability to develop cross-platform applications and user interfaces for drone control and data visualization
Deliverables
This project involved developing an Arduino-based drone platform, designed for educational purposes and equipped with sensors like cameras, GPS, sonar, and LiDAR. A comprehensive set of introductory lessons and educational materials were created to engage students in hands-on learning using the developed drone platform.