Robotic Fleet Provisioning Using Rule-Based Resource Configuration
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Solution Overview
Problem
The proliferation of data from numerous sensors 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, where queries are stored on a dynamic ledger, generating approximate responses based on summary data, and transmitting these responses, with the option to use a blockchain for data storage and neural networks for probability distribution modeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If centralized data collection methods are used to gather data from numerous sensors in value chain networks, then complete data collection is achieved, but network overhead and complexity increase significantly
Solution Approach 1:
The patent segments the centralized data collection system into distributed edge devices that operate autonomously. Each edge device processes and analyzes data locally within its own network segment, eliminating the need for all data to be transmitted to a central server. This segmentation reduces network overhead while maintaining comprehensive data collection through coordinated edge devices.
Solution Approach 2:
The patent implements local quality by enabling each edge device to perform data processing and decision-making locally rather than relying on centralized processing. Each edge device is equipped with the capability to analyze data in its immediate vicinity, making localized decisions without contributing to overall network complexity.
2Reliability
If all sensor data is transmitted to centralized systems for processing, then accurate decision-making is achieved, but transmission time and response delay increase
Solution Approach 1:
The patent applies preliminary action by pre-configuring edge devices with data processing capabilities and decision-making algorithms before data arrives. Edge devices are prepared in advance to immediately process incoming sensor data and generate decisions without waiting for centralized system instructions, thereby reducing response delay while maintaining accuracy through pre-loaded processing rules.
Solution Approach 2:
The patent introduces edge devices as intermediaries between sensors and centralized systems. These intermediaries process and filter data locally, transmitting only essential information to centralized systems. This intermediary layer reduces transmission time for critical decisions while maintaining accuracy through multi-layer validation.
3Loss of information
If detailed data from all sensors is processed centrally, then comprehensive insights are obtained, but processing complexity and computational load increase
Solution Approach 1:
The patent extracts and removes unnecessary data elements at the edge device level before transmission to centralized systems. Each edge device performs initial filtering and extraction of only the most relevant features from raw sensor data, eliminating redundant information. This extraction process reduces processing complexity at centralized systems while preserving comprehensive insights through selective data retention.
Solution Approach 2:
The patent implements partial action by having edge devices perform initial data processing and filtering locally, handling the bulk of computational work at the distributed level. Centralized systems then receive pre-processed data requiring minimal additional processing. This partial distribution of processing tasks reduces overall system complexity while maintaining comprehensive insight generation.
Data Source
AI summary
A method of provisioning robotic fleet resources 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 that are to be performed in performance of the job. The method includes determining a robotic fleet configuration data structure corresponding to the job based on the set of tasks and a fleet resource inventory that indicates fleet resources. The method includes determining a respective provisioning configuration for each respective fleet resource. The method includes provisioning the respective fleet resource based on the respective provisioning configuration and a set of resource provisioning rules that are accessible to an intelligence layer to ensure that provisioned resources comply with the provisioning rules. The method includes deploying the robotic fleet to perform the job.