Robotic Fleet Provisioning for Additive Manufacturing Tasks
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Solution Overview
Problem
The proliferation of data from numerous sensors in value chain networks overwhelms centralized systems, leading to complexity and inefficiencies in data management and decision-making, as current approaches are limited by bandwidth, storage, and processing constraints.
Innovation Solution
A method involving edge devices that process queries by storing them on a dynamic ledger, generating approximate responses using summary data, and transmitting these responses, while also utilizing probability distribution models and neural networks to optimize data retrieval and management in distributed databases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If centralized systems are used to manage data from numerous sensors, then comprehensive data management is achieved, but system complexity and processing load increase significantly
Solution Approach 1:
The patent segments the centralized data management system into distributed edge devices that process and manage sensor data locally. Each edge device handles a specific subset of sensors and data types, dividing the overall system complexity into manageable modular units that can operate independently while contributing to the global data management objective.
Solution Approach 2:
The patent introduces a hierarchical dimension to the data management architecture by adding edge devices as an intermediate layer between sensors and centralized systems. This creates a multi-level structure where data can be processed at multiple dimensions (local edge level and central level), allowing comprehensive management while distributing the processing load across different hierarchical levels.
2Loss of information
If all sensor data is transmitted to centralized systems, then complete information is available, but bandwidth and storage constraints are overwhelmed
Solution Approach 1:
The patent extracts and processes critical information from raw sensor data at the edge devices before transmission to centralized systems. Edge devices filter, aggregate, and pre-process data to extract only the most relevant information, removing unnecessary data volume while preserving essential information content for centralized analysis.
Solution Approach 2:
The patent performs preliminary data processing, filtering, and aggregation actions at edge devices before data reaches centralized systems. This preliminary action reduces the volume of data that needs to be stored and processed centrally while ensuring that important information is already prepared and organized for effective utilization.
3Reliability
If centralized processing handles all queries, then consistent data retrieval is ensured, but processing time and response delay increase
Solution Approach 1:
The patent segments query processing functionality across edge devices and centralized systems. Edge devices handle local query processing for time-sensitive operations, while centralized systems handle complex queries requiring global data consistency. This segmentation allows parallel query processing at different levels, reducing overall response time while maintaining consistency through coordinated validation.
Solution Approach 2:
The patent introduces edge devices as intermediary components between query sources and centralized data storage. These intermediaries cache frequently accessed data and handle routine queries locally, acting as a buffer that reduces direct access to centralized systems. This intermediary layer maintains data consistency through synchronization protocols while significantly reducing query response time for common operations.
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.