Centralized Robot Feature Mapping With Specialized Neural Networks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems face challenges in efficiently and cost-effectively training neural networks to identify a substantial number of features across various environments, as training a single neural network to recognize multiple features is impractical due to the high cost and complexity of gathering large training datasets.
Innovation Solution
A centralized server system that utilizes a network of robots to collect and process feature data, employing a system of neural networks trained for specific tasks, where the server selects the appropriate neural networks based on context and feature data to enhance feature identification, localization, and mapping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single neural network is trained to recognize multiple features across various environments, then feature identification capability is improved, but training cost and complexity increase substantially
Solution Approach 1:
The patent divides the training task into multiple specialized neural networks, each trained to recognize a specific feature type (e.g., one network for detecting people, another for vehicles, another for objects). This segmentation allows each network to be trained on smaller, focused datasets rather than requiring one massive network to learn all features simultaneously, thereby reducing overall training complexity while maintaining comprehensive feature identification capability
Solution Approach 2:
The system creates a universal framework that integrates multiple specialized neural networks into a single multi-functional system. The server orchestrates multiple specialized networks (each with specific expertise) to work together as a unified system capable of identifying diverse features across various environments, achieving versatility without requiring each individual network to be universally trained
2Adaptability or versatility
If a single neural network is trained to recognize multiple features, then feature identification capability is improved, but training cost increases substantially
Solution Approach 1:
The training data requirement is segmented across multiple specialized networks. Instead of gathering one enormous dataset containing all possible features for a single network, the system divides the data collection effort into smaller, targeted datasets for each feature type. Each specialized network requires less training data, reducing the total quantity of training data needed while maintaining comprehensive feature recognition capability
Solution Approach 2:
The system uses multiple copies of specialized neural networks, each optimized for specific feature types. Rather than creating one massive network that requires enormous training data, the approach creates several smaller network copies, each trained on smaller datasets specific to their feature domain, thereby reducing the total training data burden while achieving the same overall identification capability
3Measurement precision
If robots collect and transmit all image data to a centralized server, then feature detection accuracy is improved, but data transmission time and network load increase
Solution Approach 1:
The system extracts and processes only the essential feature data from images rather than transmitting complete high-resolution images to the server. Robots use local processing to identify and extract relevant feature information (such as object presence, location, and basic characteristics), then transmit only this extracted data to the centralized server for further analysis, significantly reducing transmission time and network load while maintaining detection accuracy
Solution Approach 2:
The robots perform preliminary feature detection and data processing locally before transmitting results to the server. By conducting initial image analysis and feature extraction at the robot level, the system prepares data in advance, reducing the amount of raw data that needs to be transmitted and allowing the server to focus computational resources on higher-level analysis rather than basic feature detection
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.