Lectures
Engineering 100-400: Introduction (Autonomous Vehicles)
Overview of course themes and the autonomy stack.
Lecture 1: Autonomous Vehicles: Abstraction → Automation
Lecture 1 slides with interactive elements.
Lecture 2: Software, Compilers, Interpreters & Debugging
Slides generated from l12.software.av.pptx (Reveal.js).
Lecture 3: Robotics Overview (PPTX)
Direct link to roboticsoverview.av.pptx.
Lecture 4: Technical Projects in Teams
Getting started: challenges, best practices, planning and execution.
Lecture 5: Control and Filters
Filter demo and control resources for ENG100.
Lecture 7: Computer Vision for Robots
Slides with interactive apps: filters, edges, convolution, point clouds, YOLO, and neural nets.
Extras
Build and step through a finite‑state machine.
Explore intuition behind Kalman gain and updates.
Circuit Intro: Voltage, Current, and Elements
Interactive introduction to voltage, current, and resistance.
Interactive Blink example embedded via Wokwi.
Write a right-wall follower in Python; runs in the browser.
Hands‑on modules for planning, vision, graphs, and design trade‑offs.
Demonstration of PID control.
Lecture 7: Interactive Apps
Position the line and robot; tune threshold and controller; animated sensor LEDs.
Grayscale, brightness/contrast, box/Gaussian blur, sharpen, threshold, and morphology.
Edit 3×3 kernels; try blur, sharpen, edge, and emboss with proper normalization.
Gradient magnitude or thresholded edges; optional pre‑blur.
Point Cloud Demo (RANSAC Plane)
Fit a plane, highlight inliers, and view a shaded plane patch; auto‑fit view.
YOLO Object Detection — Showcase
Pre‑canned detections on local images (no heavy model in‑browser).
HSV sliders with a color wheel to isolate the orange ping‑pong ball.
Tiny Neural Net — Forward & Backprop
Train a 2→3→1 MLP on XOR/AND/OR with live weights and loss plot.