Multi-Sensor Food Recognition for Autonomous Kitchen Preparation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current food preparation equipment in commercial kitchens is limited in its ability to handle multiple types of food, requires extensive labor, is prone to mechanical failures, and occupies valuable kitchen space, while also being costly and inefficient in preparing varied menu items due to batch processing and mechanical complexity.
Innovation Solution
An automated kitchen assistant system utilizing a combination of sensors, including an IR camera, RGB camera, and depth sensor, to identify and locate food items and kitchen objects, with a robotic arm capable of performing food preparation tasks autonomously, guided by a trained neural network to improve accuracy and reduce human error.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated food preparation equipment is used to improve productivity and reduce labor, then food preparation efficiency increases, but device complexity increases leading to higher failure rates
Solution Approach 1:
The patent replaces complex mechanical food preparation systems with a sensor-based detection system comprising IR camera, RGB camera, and depth sensor that use optical and thermal fields instead of mechanical contact. The neural network processes sensor data to identify food items and determine preparation parameters, eliminating the need for complex mechanical adjustment mechanisms while maintaining high productivity.
Solution Approach 2:
The patent creates a digital copy of the food item through multi-sensor imaging that captures thermal, visual, and depth information. This digital replica is processed by a neural network to determine food type, maturity, and preparation parameters, replacing the need for physical inspection and manual decision-making while reducing mechanical complexity.
2Measurement precision
If multi-sensor arrays with neural networks are implemented to improve food recognition accuracy, then measurement precision increases, but device complexity and cost increase
Solution Approach 1:
The patent combines three different sensor types (IR camera, RGB camera, depth sensor) into a unified multi-sensor array system. The sensors are spatially and functionally integrated to capture complementary information about food items simultaneously, achieving high measurement precision through data fusion rather than requiring individually complex sensing mechanisms.
Solution Approach 2:
The multi-sensor array serves multiple functions: the IR camera detects thermal properties and moisture content, the RGB camera captures visual appearance and color, and the depth sensor measures geometry and position. This universal sensor system handles diverse food types and preparation stages with a single integrated setup, reducing overall system complexity despite the multiple sensor components.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system enables efficient, accurate, and autonomous food preparation for multiple types of food on various cooking equipment, reducing labor costs and equipment failures, while optimizing kitchen space and improving food consistency and variety.
Implementation Method 1
a first sensor which is an Infrared (IR) camera and generates infrared (IR) image data
Data Source
AI summary
An automated kitchen assistant system inspects a food preparation area in the kitchen environment using a novel sensor combination. The combination of sensors includes an Infrared (IR) camera that generates IR image data and at least one secondary sensor that generates secondary image data. The IR image data and secondary image data are processed to obtain combined image data. A trained convolutional neural network is employed to automatically compute an output based on the combined image data. The output includes information about the identity and the location of the food item. The output may further be utilized to command a robotic arm, kitchen worker, or otherwise assist in food preparation. Related methods are also described.