Cloud-Based Material Sorting for Multi-Sensor Object Recognition
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Solution Overview
Problem
Current sorting facilities face challenges in efficiently identifying and sorting diverse materials due to limited data capture and lack of centralized artificial intelligence, leading to performance and cost bottlenecks, as well as inefficiencies in material recognition and handling.
Innovation Solution
A cloud and facility-based machine learning system that enables distributed object recognition and sorting across multiple facilities, using a cloud sorting server to train and deploy machine learning models that can adapt to various materials and facilities, leveraging diverse data sets and multiple sensors for accurate identification and sorting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a centralized machine learning system is implemented across multiple sorting facilities, then sorting accuracy and material identification efficiency are improved, but system complexity and infrastructure requirements increase
Solution Approach 1:
The patent combines multiple independent sorting facility systems into a unified cloud-based machine learning platform. Data from multiple facilities is aggregated and processed centrally to train improved machine learning models that are then deployed back to the facilities, creating a synergistic system where the whole is greater than the sum of its parts.
Solution Approach 2:
The centralized machine learning system serves multiple sorting facilities simultaneously, providing a universal platform that can identify and sort diverse materials across different locations. The system handles multiple functions including data collection, model training, model deployment, and real-time sorting decisions across the entire network of facilities.
2Productivity
If diverse materials are processed with limited sensor data, then sorting speed is maintained, but identification accuracy and recovery rate decrease
Solution Approach 1:
The system performs preliminary data collection and model training in advance. Historical data from multiple facilities is gathered and used to pre-train machine learning models before they are deployed. This preliminary action ensures that when materials are actually sorted, the system is already optimized for high-speed accurate identification without needing to process every detail in real-time.
Solution Approach 2:
The centralized cloud platform acts as an intermediary between the sensor data and the sorting decision-making process. It aggregates data from multiple sources, processes it through sophisticated machine learning models, and returns refined identification results to the sorting facilities, effectively mediating between limited raw data and high-accuracy requirements.
3Adaptability or versatility
If custom machine learning models are trained for each sorting facility, then local material characteristics are optimized, but development costs and time requirements increase
Solution Approach 1:
Instead of training completely new models for each facility, the system creates copies of a centralized base model and fine-tunes them with local data. The pre-trained models serve as templates that can be rapidly adapted to local conditions through transfer learning, dramatically reducing development time while maintaining local optimization.
Solution Approach 2:
The system discards the approach of training models from scratch for each facility and recovers computational resources by using a shared centralized training process. Local facilities contribute their data to the centralized pool, and in return receive optimized models, effectively discarding redundant development efforts and recovering resources through shared learning.
4Measurement precision
If multiple sensors and data sources are integrated, then material identification capability is enhanced, but data processing complexity and computational requirements increase
Solution Approach 1:
The system segments the data processing workload by distributing different functions across the network. Individual facilities collect and pre-process their own sensor data locally, then contribute aggregated results to the centralized cloud platform for model training. This segmentation reduces the computational burden on any single system while maintaining the benefits of multiple data sources.
Data Source
AI summary
Heterogenous material sorting is disclosed, including: identifying a first target object associated with a first object type on a surface based at least in part on a first sensed signal; providing a first control signal to a first sorting device to cause the first sorting device to remove the first target object from the surface, wherein the first sorting device is configured to manipulate objects associated with the first object type; identifying a second target object associated with a second object type on the surface based at least in part on a second sensed signal; and providing a second control signal to a second sorting device to cause the second sorting device to remove the second target object from the surface, wherein the second sorting device is configured to manipulate objects associated with the second object type.