Robotic Fleet Provisioning for Additive Manufacturing Jobs
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Solution Overview
Problem
The proliferation of data from distributed sensors and devices in value chain networks overwhelms the ability to transmit and process data effectively, leading to complexity and missed opportunities for timely insights and efficient operations.
Innovation Solution
A method for processing queries in a distributed database using edge devices, involving dynamic ledgers and probability distribution models to generate approximate responses based on summary data, reducing the need for centralized data collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data is collected and transmitted from distributed sensors and devices through centralized systems, then complete data sets are obtained for analysis, but network bandwidth is overwhelmed and data processing becomes inefficient
Solution Approach 1:
The patent segments the centralized data processing system into distributed edge devices that each process local data independently. Edge queries are executed at edge devices rather than transmitting all raw data to centralized systems, dividing the data processing workload across multiple autonomous nodes in the network.
Solution Approach 2:
The patent introduces a new dimension of data processing by implementing edge computing capabilities at distributed devices. Instead of single-dimensional centralized processing, the system operates across multiple dimensions with edge devices handling local queries and transmitting only aggregated results, adding spatial and functional layers to the data architecture.
2Measurement precision
If all raw data from distributed devices is transmitted to centralized systems for processing, then accurate insights can be generated, but network bandwidth and processing capacity are exceeded
Solution Approach 1:
The patent extracts only the essential aggregated data from edge devices rather than transmitting complete raw data sets. Edge queries process and summarize local data, extracting key metrics and insights that are then transmitted to centralized systems, removing unnecessary data volume while preserving analytical value.
Solution Approach 2:
The patent applies partial action by transmitting only the necessary aggregated results rather than complete data sets. Edge devices perform sufficient processing to generate meaningful insights without over-processing, sending selective information that maintains accuracy while reducing network load.
3Loss of information
If centralized systems process all data from distributed devices, then comprehensive analysis is achieved, but response time increases and timely decisions are delayed
Solution Approach 1:
The patent performs preliminary data processing and aggregation at edge devices before data reaches centralized systems. Edge queries execute local analysis and prepare summarized results in advance, so when data is transmitted to centralized systems, the bulk of processing is already complete, reducing overall response time while maintaining analytical depth.
Data Source
AI summary
A method of configuring robot fleets with additive manufacturing capabilities includes receiving a request for a robotic fleet to perform a job and determining a job definition data structure based on the request. The job definition data structure defines a set of tasks to be performed in furtherance of the job. The method includes determining a provisioning configuration for each additive manufacturing system based on the task to which the additive manufacturing system is assigned, the set of 3D printing requirements, the printing instructions, and the status of the additive manufacturing system. The method includes provisioning the additive manufacturing system based on the provisioning configuration and a set of additive manufacturing system provisioning rules that are accessible to an intelligence layer to ensure that provisioned systems comply with the provisioning rules. The method includes deploying the robotic fleet based on the robotic fleet configuration data structure to perform the job.