Multi-Purpose Robot Fleet Configuration for Task-Based Deployment
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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 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 query processing and reduce network overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If centralized data collection methods are used to gather data from numerous sensors, then comprehensive data availability is improved, but system complexity and transmission overhead increase significantly
Solution Approach 1:
The patent divides the centralized data collection system into distributed edge devices that independently process and store data locally. Each edge device segments the overall data processing task, eliminating the need for a single centralized collection point and reducing system complexity while maintaining comprehensive data availability across the network.
Solution Approach 2:
The patent introduces a new dimension of data storage by implementing distributed ledger technology across multiple edge devices. This transforms the traditional single-point centralized storage into a multi-dimensional distributed storage architecture, where data is replicated and stored across numerous nodes, thereby reducing complexity at any single point while preserving complete data availability.
2Measurement precision
If all sensor data is transmitted to centralized systems for processing, then data accuracy is improved, but response time and processing efficiency deteriorate
Solution Approach 1:
The patent implements preliminary data processing and validation at the edge devices before data is transmitted or used. Edge devices perform initial filtering, aggregation, and validation of sensor data locally, ensuring data accuracy is maintained while reducing the time required for transmission and centralized processing. This preliminary action eliminates the need to wait for centralized systems to process all raw data.
3Measurement precision
If centralized systems process all queries from the distributed database, then query accuracy is improved, but network overhead and processing load increase
Solution Approach 1:
The patent enables edge devices to independently execute queries against their local distributed database entries. Each edge device possesses the necessary data and processing capability to handle queries locally, eliminating the need for all queries to be routed through centralized systems. This local quality approach maintains query accuracy while dramatically reducing network overhead and processing load on centralized infrastructure.
Data Source
AI summary
A method includes receiving a request for a robotic fleet to perform a job and defining a set of tasks that are to be performed in performance of the job. The method includes assigning robots selected from a robot inventory to the set of tasks based on a robot inventory data structure that indicates, for each robot, a status and set of baseline features. The robots include one or more assigned multi-purpose robots that can be configured for different tasks and different environments. The method includes determining a configuration for each assigned robot based on the respective task that is assigned and a components inventory. The components inventory indicates multiple components and, for each component, a status and a set of extended capabilities. The method includes configuring the one or more assigned multi-purpose robots based on the respective configurations. The method includes deploying the robotic fleet to perform the job.