Project
| # | Title | Team Members | TA | Documents | Sponsor |
|---|---|---|---|---|---|
| 62 | AI-Nutritious Culinary Assistant |
Griffin Kelley Jackson Brown Tony Liu |
Aniket Chatterjee | design_document1.pdf final_paper1.pdf photo1.png photo2.png presentation1.pptx proposal1.pdf video |
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| Team Members: - Jackson Brown (jcb10) - Griffin Kelley (gkelley4) - Tony Liu (zikunl2) # Problem The processed food industry has become increasingly toxic due to chemical flavor additives (12%-32% higher cancer risk), yet cooking often intimidates new chefs when preparing. Therefore, students and working professionals may rely on convenient but unwholesome meals. Most available ‘smart cooking’ tools do not provide a real-time experience that guides users through the process from raw ingredients to the finished dish. This reduces the likelihood that the user will learn. It is also inefficient to design recipes that can be adjusted to the user's available ingredients. Users will waste food, and recipe creation is an expert skill that is difficult to customize. A healthy diet is important for increased productivity and long-term health, but is difficult to accomplish. # Solution We propose an AI-Nutritious Culinary Assistant that recognizes available ingredients and generates a personalized recipe with interactive, step-by-step guidance. Using the Meta Quest 3 as the user interface and sensor front-end, the system streams video and voice commands to an edge vision processor running an ingredient recognition pipeline. In addition to vision, the device integrates an environmental sensor module that measures ingredient weight (for portion verification) and ambient temperature (for context/safety telemetry). Finally, the appliance includes a circular rotating seasoning carousel driven by a stepper motor for proportional seasoning action and a servo-actuated gate for controlled dispensing, enabling closed-loop “dispense to target grams” assistance during cooking. # Solution Components Explain what the subsystem does. Explicitly list what sensors/components you will use in this subsystem. Include part numbers. ## Subsystem 1 - VR Headset Sensor Platform (Software) This subsystem uses the Meta Quest 3 as the primary user interface and sensing front-end. The headset’s built-in RGB cameras capture the cooking scene for ingredient recognition, and its microphone captures the user’s wake word and spoken prompts. The headset also serves as the output display, presenting step-by-step recipe instructions as an AR/VR overlay. Captured camera frames and voice transcripts/commands are transmitted to the Vision Processor subsystem for inference and planning, while the returned recipe steps and alerts are rendered back in the headset. ### Parts: Meta Quest 3 (VR headset with RGB cameras + microphone + display) Wireless link (Wi-Fi) between Quest and Jetson (stream frames + commands, receive instructions) ## Subsystem 2 - Vision Processor (Software) This subsystem performs the core perception and recipe-planning computation on the edge compute unit. It receives camera frames from the Meta Quest 3 and first detects/segments candidate ingredient regions using a real-time model (YOLO for bounding boxes, or FastSAM for masks). Each candidate region is then cropped (or masked) and passed through a CLIP-style vision encoder to generate an image embedding. In parallel, the system maintains a library of text embeddings for ingredient labels (e.g., “tomato”, “onion”, “spinach”). By comparing image embeddings to text embeddings (cosine similarity), the processor assigns the most likely ingredient label to each detected region, enabling more flexible recognition than closed-set detection alone. The final output is a structured ingredient list (label + confidence + location/mask), which is then provided to the LLM agent to generate step-by-step recipes and instructions that are sent back to the headset. ### Parts: NVIDIA Jetson Nano (edge compute unit for model inference + agent logic) Candidate region model: YOLOv8/YOLO11 (det/seg) (bounding boxes or segmentation) or FastSAM (mask proposals) Vision-language classifier: CLIP / OpenCLIP / MobileCLIP (region embedding + text embedding matching) LLM agent (recipe selection + instruction generation using detected ingredients) Communication interface: Wi-Fi (Quest ↔ Jetson) for frames/instructions; optional UART/I2C/USB (ESP32 ↔ Jetson) for weight/safety telemetry ## Subsystem 3 — Environmental Sensor Subsystem (Weight + Room Temperature) This subsystem measures ingredient mass for closed-loop dispensing and ambient temperature for environmental context (improving recipes by understanding room temperature) and essential fire hazard checks. The ESP32 reads both sensors, filters/calibrates the data (tare + scale factor), and forwards measurements to the main controller. ### Sensors/Components Load cell(s): 5 kg single-point load cell (e.g., TAL220B 5kg) or 4× half-bridge load cells (for a round platform) Load cell ADC/Amplifier: HX711 24-bit load cell amplifier breakout/module (HX711) Ambient temperature sensor: SHT31-DIS (Sensirion SHT31-DIS-B) or BME280 (Bosch BME280) over I2C MCU interface: ESP32 reads HX711 (GPIO clock/data) + temp sensor (I2C) ## Subsystem 4 — Battery Subsystem (Rechargeable Power for Portability) This subsystem powers the device from a rechargeable battery, supports charging via USB, and generates stable rails for logic and actuation. It provides regulated 3.3 V for ESP32/sensors and 5–6 V for servo/actuators while preventing brownouts during motor current spikes. ### Sensors/Components Battery pack: 1-cell LiPo, 3.7 V (e.g., 2000–5000 mAh, JST-PH) Battery charger: TP4056 1S Li-ion charger module (with protection variant preferred) or MCP73831 (Microchip MCP73831T) 3.3 V regulator (logic rail): Buck converter module (e.g., MP1584EN) set to 3.3 V or LDO if current is small 5–6 V regulator (servo rail): Buck converter module (e.g., MP1584EN) set to 5.0–6.0 V Power monitoring (optional but helpful): MAX17048 LiPo fuel gauge (MAX17048G+U) over I2C Protection/robustness: power switch + fuse/polyfuse + bulk capacitors near servo rail ## Subsystem 5 — Rotating Carousel Ring + Dispenser Subsystem (One-Piece Circular Device) This subsystem’s purposing on seasoning and provides the “lazy Susan” mechanism: a circular rotating ring that indexes ingredient pods to a fixed dispense station above the scale. A motor rotates the ring to the selected pod; a servo opens a gate to dispense into the center bowl. The ESP32 controls indexing, homing, and dispensing, using the scale feedback to stop at a target mass. ### Sensors/Components Rotation motor: NEMA 17 stepper motor (e.g., 42BYGH-class) Stepper driver: DRV8825 or A4988 stepper driver module Homing/index sensor: A3144 Hall-effect sensor + small neodymium magnet (defines “slot 0”) Dispense actuator: MG90S micro servo (metal gear) or SG90 (lighter duty) Mechanical drive: GT2 timing belt + pulley set (e.g., GT2 6mm belt, 20T pulley) or friction wheel drive Ring support: lazy-susan bearing (turntable bearing) or printed rail + small rollers/V-wheels Dispense hardware: fixed chute + passive pod gate (flap/valve) engaged by the servo at the station # Criterion For Success We’d like to set the criterion for success with numerical benchmarks: The sensor for weight measurement should have a resolution of 1 gram and a gram error range of 0 to 100 grams. Fully chargeable battery offering approximately 20 minutes of runtime. The rotating carousel dispenser should spin to the desired spot within 2 error. Ingredient classification and localization accuracy >= 85% with 10 FPS real-time performance. Eventually, the pipeline of feeding input from a VR headset (visual image stream) and sensors (hardware) to the vision processor and returning a contextual recipe back to the VR headset should be completed. ## References: [1]: Hasenböhler, Anaïs et al. “Intake of food additive preservatives and incidence of cancer: results from the NutriNet-Santé prospective cohort.” BMJ (Clinical research ed.) vol. 392 e084917. 7 Jan. 2026, doi:10.1136/bmj-2025-084917 |
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