Audrey Reinhard
Computer Engineering · UC Riverside
Passionate about robotics, embedded systems, and computer vision. I love to design and build systems that connect software with the physical world - combining sensors, algorithms, and hardware to solve real-world problems.
Where software meets the physical world
I've always been drawn to problem solving and puzzles. Building systems that connect software and hardware feels like an endless challenge — I enjoy designing solutions that interact with the real world through sensors, perception, and autonomous decision making.
Robotics & Autonomy
Path planning, sensor fusion, real-time navigation
Computer Vision
Object detection, classification, and motion tracking
Embedded Systems
AVR, microcontrollers, low-level hardware interfaces
Education
Featured Projects
A selection of systems I've designed and built - combining sensors, algorithms, and hardware.
Autonomous Object-Avoiding Robot
An autonomous robot designed for real-world sidewalk navigation. The system fuses multiple sensor modalities to perceive, plan, and react in real time without human intervention. I am currently improving upon this project.
- Computer vision pipeline using OpenCV for real-time lane centering and dynamic obstacle detection
- Ultrasonic sensor array for close-range proximity sensing with reactive collision avoidance
- GPS/IMU fusion for outdoor path planning and continuous localization
- ROS2 node architecture decoupling perception, decision-making, and actuation across separate devices for fault-isolated safety control
- Motor control firmware written in bare-metal C on an Arduino Nano, communicating over UART for low-latency actuation
Oura Ring Tamagotchi - Raspberry Pi Pico & FPGA powered
An Oura Ring powered tamagotchi style game built on a Raspberry Pi Pico and an FPGA. The Raspberry Pi Pico connects to the Oura Ring API to read the user's sleep, activity data, and readiness data, which is then used to influence the game state. Additionally, Oura Ring data is used to display graphs with daily and weekly health insights.
- 480 x 270 TFT LCD Display powered by the FPGA
- Oura Ring API integration for sleep, activity, and readiness data
- SPI communication between Pico(Controller) and FPGA(Peripheral)
- LVGL for graphics and animations
Space Invaders on AVR
A fully self-contained handheld game built from the ground up on an AVR microcontroller — no OS, no abstraction layer. Every pixel, sound, and input handled in bare-metal C.
- ST7735 SPI LCD display driver written from scratch
- Joystick input with debounce logic and analog ADC reading
- PWM-driven audio output for sound effects and music
- Custom game loop with fixed-timestep physics and collision detection
Robotics Camp
A robotic RC car with a Convolutional Neural Network (CNN) for real-time object detection. Designed and taught to UCR students in a month-long robotics camp. This project combines embedded systems, computer vision, and machine learning to create an interactive learning experience.
- CNN for real-time object detection on contrained hardware
- Raspberry Pi 4 along with AdaFruit Motor Hat for motor control
- Arduino Nano connected via Bluetooth for remote control
- Led team on designing curriculum and taught to other college students across different disciplines
- 3D Modeled and printed Chassis
Secret Journal
A Citrus Hacks 2026 project - a play on the early 2000s voice authenticating diary. The Secret Journal is a secure physical journal that uses voice and face recognition to authenticate users and protect their private thoughts.
- Built backend in Python integrating DeepFace for facial recognition and Resemblyzer for voice recognition.
- Connected backend to a web interface built with HTML, CSS, and JavaScript.
- Programmed an Arduino Nano to control a stepper motor lock, red/green LEDs, and a 16x2 LCD screen.
- Established communication between the Python backend and Arduino over UART
- Designed the physical enclosure in Fusion 360
- Integrated Gemma from Gemini for prompt suggestions based on user profile
Computer Vision Motion Detection
A real-time detection and classification system for urban micro-mobility. Built to identify and track pedestrians, cyclists, and scooters from live video streams using lightweight ML models.
- YOLO-based object detection fine-tuned on custom dataset
- TinyML deployment for edge inference on constrained hardware
- Multi-class tracking with bounding box visualization
- TensorFlow pipeline with data augmentation and validation
Chronotype Task Scheduler
A personalized task scheduling system that adapts to individual chronotypes and productivity patterns.
- Interactive chronotype quiz that dynamically assigns energy-based scheduling profiles
- Weekly calendar dashboard with drag-and-drop task rearrangement and real-time refresh
- Task entry system supporting priority, duration, due date, and task-type classification
- Persistent data storage so schedules and tasks are saved across sessions
- SOLID architecture with modular scheduling logic tested via Google Test and Qt Test
Swipe in System
A swipe in system used by the UCR Robotics lab for checking in out lab equipment. The system uses a magnetic card reader to read student IDs and logs check-in/check-out times for each piece of equipment. It also has an admin dashboard for managing inventory and generating usage reports. Connects to google sheets for data storage and real-time updates.
- Built to be deployed on Raspberry Pi in a stand-alone system.
- System integrates with a magnetic card reader for student ID verification.
- Uses Google Sheets API for data storage and real-time updates along with SQLite for local data persistence.
- Sends email notifications for check-in/check-out events and account creation.
Skills & Technologies
Languages, frameworks, and tools I work with regularly.
Let's connect
Open to internships, full-time work, and collaboration on robotics and embedded systems projects.
Ready to build something great?
Whether it's a research collaboration, internship inquiry, or just a conversation about robotics — I'd love to hear from you.
✉ Send me an email