AI Chipset Robot Fleet Configuration for Task-Specific Deployment
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Solution Overview
Problem
Existing additive manufacturing processes are inefficient, prone to product inconsistency, and unreliable, leading to increased costs and supply chain inefficiencies, while conventional machine vision systems struggle with capturing rich object information and dynamic environments, and robotics implementations fail to leverage emerging technologies for optimal robot fleet management.
Innovation Solution
A robot fleet management platform utilizing a governance-enabling intelligence layer with artificial intelligence services, digital twins, and adaptive intelligence to optimize robot fleet configuration, task ordering, and workflow simulation for improved efficiency and accuracy in additive manufacturing and supply chain management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional additive manufacturing processes are used, then manufacturing capability is provided, but efficiency is low and product consistency is poor
Solution Approach 1:
The patent implements a closed-loop feedback system where sensors monitor manufacturing parameters in real-time, digital twins simulate process outcomes, and AI models adjust parameters dynamically to maintain consistency and efficiency
Solution Approach 2:
The system performs preliminary simulations using digital twins before actual manufacturing, allowing optimization of parameters and prediction of outcomes to ensure consistent results without rework
2Loss of information
If more data is collected from IoT sensors and systems, then insights opportunities increase, but complexity and volume overwhelm users
Solution Approach 1:
The patent introduces AI models and digital twins as intermediaries that automatically process, analyze, and translate raw data from multiple sources into actionable insights, eliminating the need for users to directly manage complex data volumes
Solution Approach 2:
The system automatically performs data collection, processing, analysis, and insight generation without requiring user intervention in data management tasks, allowing users to directly access insights
3Adaptability or versatility
If robot fleet management uses conventional methods, then basic operations are performed, but emerging technologies are not leveraged for optimal management
Solution Approach 1:
The patent creates a universal management platform that integrates multiple emerging technologies (AI, digital twins, blockchain, IoT) into a single system that can manage diverse robot fleets across different manufacturing contexts
Solution Approach 2:
The system replaces conventional mechanical and manual management approaches with intelligent software-based control, using AI models and digital twins to automate decision-making and coordination
Data Source
AI summary
A method of configuring a robot of a fleet of robots for use of an AI chipset includes receiving a request for a robotic fleet to perform a job. The method includes defining a set of tasks that are to be performed by the robotic fleet in performance of the job. The method includes assigning at least one task of the set of tasks to a robot. The method includes determining a configuration for the robot based on the assigned task and a components inventory that indicates different components that can be provisioned to the robot including at least one AI chipset, and for each component, a set of extended capabilities and a status of the component. The method includes configuring the robot based on the determined configuration to use the at least one AI chipset. The method includes deploying the robotic fleet to perform the job.