Multi-sensor array including an IR camera as part of an automated kitchen assistant system for recognizing and preparing food and related methods
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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 significant space, leading to increased costs and customer dissatisfaction due to human error and inefficiencies.
Innovation Solution
An automated kitchen assistant system utilizing a combination of sensors, including an IR camera, RGB camera, and depth sensor, to recognize and prepare various food items, with a robotic arm capable of performing cooking tasks autonomously based on sensor data and neural network processing, reducing the need for extensive worker involvement and improving cooking consistency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated food preparation equipment is used, then productivity and consistency are improved, but device complexity and upfront costs increase
Solution Approach 1:
The system divides the complex food preparation task into discrete, manageable steps (sensing, identification, cooking parameter selection, execution). Each component (sensor array, processor, robotic arm, cooking appliance) handles a specific segment of the process, reducing overall system complexity while maintaining high productivity.
Solution Approach 2:
The sensor array and processing system are designed to handle multiple types of food items and cooking methods through a single unified platform. The system can identify various food types and automatically adjust cooking parameters, eliminating the need for separate specialized equipment for each food type and thereby reducing device complexity.
2Manufacturing precision
If specialized food preparation equipment is used, then cooking precision is improved, but adaptability to different food types deteriorates
Solution Approach 1:
The system employs sensor arrays that continuously monitor food items during preparation and provide feedback to the processing system. This real-time feedback enables automatic adjustment of cooking parameters to maintain precision across different food types, eliminating the need for specialized equipment for each food type.
Solution Approach 2:
The system maintains a library of cooking parameters for different food types and dynamically selects and adjusts parameters based on sensor identification. This allows a single piece of equipment to achieve cooking precision comparable to specialized equipment while being adaptable to various food types.
3Productivity
If batch preparation equipment is used, then productivity is improved, but loss of time in setup and batch processing increases
Solution Approach 1:
The sensor array continuously monitors the food preparation area and identifies food items before they are fully placed in the cooking appliance. The system pre-processes sensor data and prepares cooking parameters in advance, eliminating setup time between batches and enabling seamless continuous processing.
Solution Approach 2:
The system transitions from discrete batch processing to continuous processing by maintaining constant sensor monitoring and automatic appliance operation. Food items are processed as they enter the cooking area without interruption, eliminating idle time between batches while maintaining high throughput.
4Ease of operation
If conventional food preparation equipment is used, then ease of operation is maintained, but reliability deteriorates due to mechanical failures
Solution Approach 1:
The system performs self-monitoring through sensor arrays that detect food items and cooking conditions automatically. The processing system autonomously selects cooking parameters and controls the robotic arm and appliance, eliminating the need for manual operation while ensuring consistent reliable performance without mechanical failure points associated with complex manual controls.
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 preparation of multiple food types with reduced labor requirements, enhanced consistency, and minimized equipment failures, while optimizing kitchen space, thereby improving food quality and reducing costs.
Implementation Method 1
A first sensor of the combination of sensors is an Infrared (IR) camera that generates first 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.