Robotic Fleet Provisioning for On-Demand 3D Printing 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
1Quantity of substance
If centralized systems are used to manage data from numerous sensors, then comprehensive data collection is achieved, but system complexity and processing burden increase significantly
Solution Approach 1:
The patent segments the centralized data management system into distributed edge devices that autonomously process and filter sensor data locally. Each edge device handles a portion of the data processing task, reducing the burden on any single system component and distributing complexity across multiple nodes in the network.
Solution Approach 2:
The patent extracts non-essential processing functions from the centralized system and relocates them to edge devices. By taking out data filtering, aggregation, and preliminary analysis functions from the central system and implementing them at the edge, the patent reduces the processing burden on centralized systems while maintaining comprehensive data collection.
2Loss of information
If all sensor data is transmitted to centralized systems, then complete information availability is achieved, but bandwidth and storage constraints are overwhelmed
Solution Approach 1:
The patent extracts redundant and non-critical data elements before transmission to centralized systems. Edge devices perform local filtering to remove duplicate sensor readings, filter out noise, and eliminate unnecessary data points, thereby reducing the volume of data that needs to be transmitted and stored centrally while preserving essential information.
Solution Approach 2:
The patent performs preliminary data processing, aggregation, and filtering at the edge devices before data is transmitted to centralized systems. By pre-processing data locally to consolidate information and remove redundancies, the patent reduces the amount of data that needs to be handled by centralized systems while ensuring that complete and accurate information is preserved.
3Stability of the object's composition
If centralized processing is used for all data, then uniform decision-making is achieved, but response time and efficiency decrease
Solution Approach 1:
The patent segments decision-making authority between edge devices and centralized systems. Edge devices autonomously make local decisions for time-critical operations based on real-time sensor data, while centralized systems handle strategic decisions requiring comprehensive analysis. This segmentation enables both rapid local responses and consistent centralized coordination.
Solution Approach 2:
The patent implements preliminary data processing and analysis at edge devices to prepare information for centralized decision-making. By pre-filtering, aggregating, and validating data locally before transmission, the patent enables centralized systems to make consistent decisions more efficiently, as the incoming data is already processed and organized.
Data Source
AI summary
A robotic fleet platform for configuring robot fleets with additive manufacturing capabilities includes a fleet resources data store that maintains a fleet resource inventory indicating additive manufacturing systems that can be provisioned with fleet resources and, for each additive manufacturing system, a set of 3D printing requirements, printing instructions that define configuring an on-demand production system for 3D printing, and a status of the additive manufacturing system. The platform includes additive manufacturing system provisioning rules that are accessible to an intelligence layer to ensure that provisioned additive manufacturing systems comply with the provisioning rules. The platform receives a request for a robotic fleet to perform a job and determine a job definition data structure based on the request. The job definition data structure defines a set of tasks for the job. The platform deploys the robotic fleet based on the robotic fleet configuration data structure to perform the job.