Centralized Multi-Robot Feature Detection With Neural Network Segmentation
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Solution Overview
Problem
Training neural networks to identify a substantial number of features in complex environments, such as retail stores, is costly and impractical due to the time and labor required for large training datasets.
Innovation Solution
A centralized server system that integrates data from multiple robots, utilizing a network of neural networks to enhance feature identification, localization, and reliability, allowing for efficient processing and analysis of feature data across a plurality of environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single robot is used to collect feature data, then device complexity is reduced, but measurement precision and reliability of feature identification deteriorate
Solution Approach 1:
The patent combines data from multiple robots to improve feature identification accuracy. The server receives and integrates feature data from multiple robots operating in the same environment, allowing for cross-validation and more reliable detection of features such as products on shelves. This merging of multiple data sources resolves the contradiction by maintaining simple individual robot designs while achieving high measurement precision through collective data analysis.
2Adaptability or versatility
If multiple neural networks are deployed to identify diverse features, then feature identification capability improves, but processing time and computational resources increase
Solution Approach 1:
The patent segments the feature identification task by deploying specialized neural networks for different types of features. Each neural network is trained to identify specific categories of features (e.g., products, pricing labels, promotional materials), allowing for parallel processing of different feature types. This segmentation enables the system to maintain high adaptability across diverse feature categories while reducing overall processing time through concurrent execution of specialized networks rather than using a single general-purpose network.
Solution Approach 2:
The system employs multiple neural networks that may identify more features than strictly necessary, allowing for selective filtering and prioritization of results. This approach ensures comprehensive feature detection across all categories while enabling the system to focus computational resources on the most relevant features for a given task, effectively managing processing time while maintaining versatility.
3Measurement precision
If large training datasets are used to train neural networks, then feature identification accuracy improves, but training cost and time increase
Solution Approach 1:
The patent creates neural networks with universal training data that can identify multiple types of features across different retail environments. Instead of training separate networks for each feature type or environment, the system uses diverse training datasets that encompass various products, store layouts, lighting conditions, and feature categories. This universal training approach achieves high identification accuracy across diverse scenarios while reducing the need for extensive retraining, thereby improving training efficiency and productivity.
4Reliability
If data from multiple robots is integrated centrally, then feature detection reliability improves, but system complexity and data processing requirements increase
Solution Approach 1:
The patent introduces a centralized server as an intermediary that manages the integration of data from multiple robots. The server receives feature data from multiple robots, performs centralized processing and validation, and generates consolidated results. This intermediary architecture improves detection reliability through cross-validation of features detected by different robots while managing system complexity by centralizing the complex processing logic in a dedicated server rather than requiring complex peer-to-peer coordination between robots.
Data Source
AI summary
Systems and methods for detection of features within data collected by a plurality of robots by a centralized server are disclosed herein. According to at least one non-limiting exemplary embodiment, a plurality of robots may be utilized to collect a substantial amount of feature data using one or more sensors coupled thereto, wherein use of the plurality of robots to collect the feature data yields accurate localization of the feature data and consistent acquisition of the feature data. Systems and methods disclosed herein further enable a cloud server to identify a substantial number of features within the acquired feature data for purposes of generating insights. The substantial number of features far exceed a practical number of features of which a single neural network may be trained to identify.