Robot Fleet Digital Twins for Real-Time Workflow Simulation
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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 vision technologies struggle with capturing rich object information and dynamic environments, and robotics implementations fail to leverage emerging technologies for advanced robot management.
Innovation Solution
A robot fleet management platform utilizing a governance-enabling intelligence layer with AI services, digital twins, and adaptive intelligence to optimize robot fleet configurations and workflows, combined with a cloud-based management platform for value chain network entities, enabling real-time data processing and simulation for improved additive manufacturing and supply chain management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If additive manufacturing processes are used, then manufacturing flexibility and customization are improved, but manufacturing precision and product consistency deteriorate
Solution Approach 1:
The patent implements a feedback mechanism where sensors monitor the additive manufacturing process in real-time, capturing data on layer formation, material deposition, and dimensional accuracy. This feedback is fed back to the control system to automatically adjust process parameters, ensuring consistent product quality while maintaining manufacturing flexibility.
Solution Approach 2:
The patent replaces manual mechanical adjustment systems with automated computer-controlled systems that use digital models and algorithms to precisely control the additive manufacturing process. This substitution of mechanical systems with automated control systems improves both precision and consistency while maintaining adaptability.
2Loss of information
If more data is collected from IoT sensors and smart devices, then operational insights and decision-making quality are improved, but data complexity and processing burden increase
Solution Approach 1:
The patent segments the collected data into distinct categories and layers: operational data from sensors, contextual data from enterprise systems, and derived insights. This segmentation allows the system to process and analyze specific data types independently, reducing overall complexity while preserving comprehensive operational insights.
Solution Approach 2:
The patent introduces an intermediary layer of data processing and analytics that sits between the raw sensor data and the decision-making systems. This intermediary layer aggregates, filters, and translates raw data into actionable insights, reducing the burden on downstream systems while maintaining information quality.
3Device complexity
If conventional vision technologies are used for object detection, then system simplicity is maintained, but measurement precision and object information capture deteriorate
Solution Approach 1:
The patent merges multiple sensing modalities including conventional vision sensors with advanced technologies such as laser scanning, structured light, and depth sensors. This combination creates a multi-modal detection system that captures comprehensive object information with high precision while managing system complexity through integrated processing.
Solution Approach 2:
The patent transitions from two-dimensional image capture by conventional vision systems to three-dimensional spatial mapping and characterization. By adding depth and spatial dimension data through advanced sensing, the system achieves superior measurement precision and object information capture.
Data Source
AI summary
A digital twin system includes a library of different types of robot operating unit digital twins stored in a storage system. The digital twin system includes one or more interfaces through which information associated with a physical robot operating unit corresponding to an instance of the robot operating unit digital twins is communicated. The digital twin system includes a set of processors that execute a set of computer-readable instructions to collectively operate one or more execution environments for executing instances of a portion of the different types of robot operating unit digital twins. The digital twin system also generates digital twin instances for individual robot operating units, a team of robot operating units, or a fleet of robot operating units. The digital twin system simulates operation of a physical robot by executing an instance of a digital twin generated for the physical robot based on information communicated through the interfaces.