Robotic Dish Handling Using Neural Network Perception and Localization
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Solution Overview
Problem
Conventional robots lack the sophisticated perception, planning, and control capabilities to securely handle and move dishes, especially when they are collocated with diverse types of dishes in complex environments, due to their inability to detect, identify, and localize the position and orientation of dishes.
Innovation Solution
A system comprising cameras for image capture, a processor for dish detection, identification, and localization using neural networks, and a robotic arm for secure handling and movement of dishes, enabling the detection, identification, and localization of dishes to plan and execute their secure pick-up, holding, and drop-off.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If conventional robots are used to handle dishes, then the robot structure is simple and easy to manufacture, but the robot cannot detect, identify, or localize dishes in complex environments with diverse dish types
Solution Approach 1:
The perception system is segmented into three distinct neural network models: dish detection model for presence detection, dish identification model for type classification, and dish localization model for position and orientation estimation. This segmentation allows each model to specialize in one aspect of dish perception, improving overall detection accuracy while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
Multiple cameras are introduced as intermediary devices to capture images of dishes from different viewpoints. These cameras serve as mediators between the physical dish and the neural network processing system, enabling the robot to perceive dish characteristics, position, and orientation indirectly through captured images before executing handling actions.
2Reliability
If conventional robots handle dishes in open environments, then the handling process is simple, but the robot cannot securely handle dishes when they are collocated with diverse types of dishes
Solution Approach 1:
The system performs preliminary perception actions before dish handling: the detection model first identifies dish presence, then the identification model determines dish type, and finally the localization model estimates position and orientation. These preliminary actions provide the robot with comprehensive information about the dish and its environment before executing the secure handling operation, enabling reliable handling in complex collocated environments.
Solution Approach 2:
The neural network models process and transform image data through multiple parameter changes: converting pixel values to detection probabilities, classifying dishes into type categories, and estimating spatial parameters (position and orientation). These parameter transformations enable the robot to adapt to various dish types and environmental conditions, improving both handling reliability and environmental versatility.
3Measurement precision
If a robot uses basic image capture, then the system is simple, but the robot cannot accurately detect, identify, and localize dish position and orientation for secure movement
Solution Approach 1:
The patent replaces traditional mechanical or rule-based image processing systems with neural network-based computational models. The detection, identification, and localization models use machine learning algorithms to automatically extract features and estimate dish parameters from images, achieving high measurement precision for position and orientation without complex mechanical processing systems.
Data Source
AI summary
A system and method for perceiving a dish, planning its handling, and controlling its motion, comprising: capturing at least one image of a region disposed to comprise said dish using at least one camera; classifying said image using a dish detection model to determine the presence of a dish; classifying said image using a dish identification model to determine the type of said dish; estimating the position and orientation of said dish using a dish localization model; picking up, holding, or dropping off said dish securely using said type, position, and orientation of said dish with a robotic arm, whereby said dish is detected, identified, and localized to securely move it from one location to another.