Robot Fleet Governance With Digital Twins for Workflow Coordination
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Solution Overview
Problem
Existing additive manufacturing processes face inefficiencies, product inconsistencies, and unreliability, 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 advanced automation.
Innovation Solution
A robot fleet management platform utilizing a governance-enabling intelligence layer, digital twins, and adaptive intelligence services to optimize robot fleet configuration, task ordering, and workflow simulation for efficient additive manufacturing and supply chain management, integrated with a cloud-based management platform for real-time data processing and monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional additive manufacturing processes are used, then manufacturing capability is provided, but manufacturing efficiency is low and product consistency is poor
Solution Approach 1:
The patent implements a feedback mechanism where machine vision systems capture images of manufactured parts, and AI models analyze these images to provide feedback on manufacturing quality. This closed-loop feedback system enables real-time monitoring and adjustment of manufacturing parameters, improving both efficiency and product consistency by automatically detecting defects and preventing recurrence.
Solution Approach 2:
The patent replaces conventional mechanical inspection methods with AI-based machine vision systems. Instead of manual or mechanical measurement devices, the system uses computer vision and deep learning models to automatically inspect parts, enabling faster, more consistent, and contactless quality assessment that improves both manufacturing speed and precision.
2Loss of information
If conventional machine vision systems are used, then object detection is provided, but rich object information and dynamic environment capture are insufficient
Solution Approach 1:
The patent creates a universal AI platform that performs multiple functions: image capture, object detection, defect classification, root cause analysis, and manufacturing parameter optimization. This multi-functional system consolidates what would otherwise require separate systems, reducing overall complexity while enabling comprehensive information extraction from manufacturing environments.
Solution Approach 2:
The patent introduces an AI model as an intermediary between the machine vision system and the manufacturing control system. This AI intermediary processes raw images and extracts meaningful information, acting as a bridge that translates visual data into actionable manufacturing insights without requiring direct complex connections between all system components.
3Extent of automation
If robotics implementations use conventional approaches, then automation is provided, but advanced automation leveraging emerging technologies is not achieved
Solution Approach 1:
The patent implements dynamic automation where robotic systems continuously adapt to changing manufacturing conditions based on real-time AI analysis. The system dynamically adjusts automation parameters, task assignments, and process controls based on live feedback from machine vision and sensor systems, enabling flexible automation that responds to variations in materials, equipment state, and production requirements.
Solution Approach 2:
The patent merges multiple emerging technologies into a unified robotic automation system: machine vision for perception, AI/ML for decision-making, and robotics for execution. This integration creates a cohesive automated system where these technologies work together synergistically, enabling advanced automation capabilities that leverage the strengths of each technology while reducing individual system complexities.
4Loss of information
If more data is collected from IoT sensors and systems, then competitive advantage opportunities increase, but complexity and volume overwhelm users
Solution Approach 1:
The patent extracts only the most relevant information from the vast data collected by IoT sensors and manufacturing systems. The AI model selectively identifies and extracts critical features and patterns related to manufacturing quality and equipment performance, filtering out redundant or less important data. This extraction approach provides actionable insights without presenting the full complexity of the raw data volume to users.
Data Source
AI summary
A robot fleet management platform includes datastores configured to store a governance library defining governance standards. Processors execute computer-readable instructions to implement a governance-enabling intelligence layer that receives and responds to intelligence requests received from intelligence service clients. The intelligence layer includes artificial intelligence services including machine learning, rules-based intelligence, digital twin, robot process automation, and machine vision. The set of governance standards is applied to decisions made by one or more of the set of artificial intelligence services. An intelligence layer controller coordinates performance of the artificial intelligence services on behalf of the intelligence service clients and performance of analyses corresponding to the artificial intelligence services based on the set of governance standards. The intelligence layer returns decisions determined by the artificial intelligence services in response to the intelligence requests. The decisions are determined based on intelligence service data sources and the set of analyses.