Robot Fleet Configuration Using Component Inventory for Job Matching
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Solution Overview
Problem
The proliferation of data from IoT sensors and other sources in value chain networks overwhelms traditional centralized data collection methods, leading to complexity and inefficiencies in data transmission and automated decision-making.
Innovation Solution
A method for processing queries in a distributed database using edge devices, which store queries on a dynamic ledger, generate approximate responses based on summary data, and transmit these responses, leveraging technologies like blockchain and neural networks for efficient data management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If centralized data collection methods are used to gather data from IoT sensors and other sources in value chain networks, then comprehensive data coverage is achieved, but system complexity and transmission inefficiency increase
Solution Approach 1:
The patent divides the centralized data collection system into multiple distributed edge devices deployed across the value chain network. Each edge device independently collects and processes data from local IoT sensors and sources, eliminating the single-point bottleneck and reducing system complexity while maintaining comprehensive data coverage through distributed aggregation.
Solution Approach 2:
The patent transitions from a single-dimensional centralized collection architecture to a multi-dimensional distributed edge computing architecture. Data collection occurs simultaneously at multiple spatial dimensions (different edge locations) and temporal dimensions (real-time local processing), reducing transmission overhead and system complexity while maintaining comprehensive coverage.
2Loss of information
If all raw data is transmitted to centralized systems for processing, then complete data availability is ensured, but data transmission burden and processing time increase
Solution Approach 1:
The patent implements preliminary data processing and summarization at edge devices before transmission to centralized systems. Edge devices perform initial filtering, aggregation, and preprocessing of raw data, ensuring that only essential summarized data is transmitted. This maintains complete data availability for analysis while significantly reducing transmission burden and processing time.
Solution Approach 2:
The patent extracts only the essential summarized data from raw data streams at the edge, separating critical information from redundant details. This extraction process ensures that centralized systems receive only the necessary data for high-level decision-making, maintaining data availability while minimizing transmission overhead and processing time.
3Measurement precision
If detailed query processing is performed on all available data, then query accuracy is improved, but computational overhead and response time increase
Solution Approach 1:
The patent applies partial action by processing only the summarized data subset that is most relevant to specific query types. Edge devices maintain indexes and summaries of key data characteristics, allowing the system to answer many queries by examining only partial data representations rather than processing all raw data, thus maintaining acceptable query accuracy while dramatically improving response time.
Data Source
AI summary
A robot fleet management platform includes a resources data store that maintains a robot inventory indicating robots that can be assigned to a robot fleet and, for each respective robot, a set of baseline features of the robot and a respective status. The resources data store maintains a components inventory indicating different components that can be provisioned to one or more multi-purpose robots and, for each component, a respective set of extended capabilities corresponding to the component and a respective status. The robot fleet management platform receives a request for a robotic fleet to perform a job, determines a job definition data structure based on the request, determines a robot fleet configuration data structure corresponding to the job based on the set of tasks and the robot inventory, determines a respective configuration for each assigned robot based on the components inventory, configures the assigned robots, and deploys the robotic fleet.