Centralized Robot Data Aggregation for Scalable Feature Detection
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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 utilizes a network of robots to collect and process data, enhancing feature identification reliability, consistency, and localization through neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a centralized server system with neural networks is used to identify features in complex environments, then feature identification reliability and consistency are improved, but the time and labor required for training datasets increases
Solution Approach 1:
The system performs preliminary actions by having robots collect and organize spatial data, images, and feature information in advance before neural network training is needed. This pre-collected structured data serves as ready-to-use training material, reducing the time required for actual training while maintaining reliability through pre-validated data quality
Solution Approach 2:
The centralized server acts as an intermediary between multiple robots and the neural network training process. It aggregates data from multiple robots, processes it into standardized formats, and prepares comprehensive training datasets, thereby reducing the individual burden on each robot and accelerating the overall training process while ensuring data consistency
2Reliability
If multiple robots collect and transmit data to a centralized server, then feature identification consistency is improved, but data processing complexity increases
Solution Approach 1:
The system enforces homogeneity by standardizing data formats, spatial coordinate systems, and feature representation methods across all robots. The centralized server implements uniform processing protocols that ensure consistent data structures from multiple sources, making aggregation and training straightforward while maintaining feature identification consistency
Solution Approach 2:
The data processing workload is segmented and distributed across multiple robots that independently collect and pre-process data locally. Each robot handles its own data acquisition and initial processing, then transmits processed results to the centralized server, which performs final aggregation. This segmentation reduces the complexity burden on any single device while maintaining overall system consistency
3Adaptability or versatility
If neural networks are trained to identify a substantial number of features, then feature identification capabilities are enhanced, but the cost and impracticality due to large training datasets increases
Solution Approach 1:
The system implements universality by creating a multi-functional data collection framework where robots perform multiple tasks: spatial mapping, feature detection, image capture, and data annotation. The centralized server processes this multi-purpose data into universal training datasets that can be used across different feature identification tasks, reducing the need for separate specialized datasets for each feature type and lowering overall preparation costs
Solution Approach 2:
The system merges data collection, processing, and training dataset creation into an integrated workflow. Multiple robots simultaneously collect various types of data (spatial, visual, feature-related) that are then combined and processed by the centralized server into unified training datasets. This merging eliminates redundant separate data collection processes and reduces the overall effort required to prepare comprehensive training data for multiple feature types
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.