Robot Fleet Workflow Simulation for Value Chain Task Allocation
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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 optimal robot fleet management.
Innovation Solution
A robot fleet management platform with a governance library, intelligence layer, and simulation system that utilizes artificial intelligence and digital twins to optimize robot fleet configuration, task ordering, and workflow simulation, integrating with additive manufacturing and network connectivity for enhanced decision-making and resource 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, product consistency is poor, and reliability is reduced
Solution Approach 1:
The system performs workflow simulation and digital twin validation before actual manufacturing execution. The simulation environment pre-validates process parameters, robot configurations, and workflow sequences, identifying potential issues before physical production begins. This preliminary action ensures manufacturing consistency while maintaining efficiency through optimized parameter selection.
Solution Approach 2:
The system implements continuous feedback loops through sensor data collection during manufacturing, comparing actual process parameters against simulated models. Real-time adjustments are made based on feedback from digital twins and simulation results, ensuring product consistency while optimizing manufacturing efficiency through adaptive parameter tuning.
2Loss of information
If more data is collected from IoT sensors and systems, then opportunities for insight increase, but complexity and volume overwhelm users
Solution Approach 1:
The system extracts and separates critical insights from the overwhelming data volume through AI-powered analysis. Digital twins and simulation systems isolate key process parameters and performance metrics from raw sensor data, presenting only relevant insights to users. This extraction approach maintains full data collection benefits while eliminating information overload.
Solution Approach 2:
The system introduces AI algorithms and digital twin models as intermediaries between raw data and user decision-making. These intermediaries process, filter, and translate complex multi-source data into actionable insights, reducing the cognitive burden on users while preserving all underlying information for analysis.
3Extent of automation
If robot fleet management uses traditional methods, then basic automation is achieved, but emerging technologies like AI and digital twins are not leveraged for optimal management
Solution Approach 1:
The system creates a universal robot fleet management platform that integrates multiple emerging technologies including AI, digital twins, simulation, and IoT. This multi-functional system simultaneously provides basic automation while incorporating advanced capabilities for predictive maintenance, workflow optimization, and adaptive task allocation, making the automation system versatile and future-proof.
Solution Approach 2:
The system implements dynamic robot fleet management where AI algorithms continuously adapt task allocations, workflow sequences, and resource configurations based on real-time conditions. Digital twins enable dynamic simulation and validation of changes before implementation, allowing the automation system to evolve and optimize continuously rather than following fixed traditional methods.
4Productivity
If workflow simulation and digital twins are implemented, then manufacturing efficiency and accuracy improve, but system complexity increases
Solution Approach 1:
The system creates virtual digital twin copies of physical manufacturing systems, robots, and workflows. These digital replicas enable simulation and validation without affecting actual production. By copying system behavior into virtual environments, the platform achieves advanced simulation capabilities while isolating complexity from the physical manufacturing operations.
Solution Approach 2:
The system segments the manufacturing system into discrete digital twin models for different components (robots, workcells, processes). Each digital twin can be simulated and validated independently before integration. This segmentation approach manages system complexity by breaking down the overall system into manageable modular units that can be developed and validated separately.
Data Source
AI summary
A robot fleet management platform includes one or more processors configured to execute instructions. The instructions include receiving a job request comprising information descriptive of job deliverable and request-specific constraints for delivering the job deliverable. The instructions include applying content and structural filters to content received in association with a job request to identify portions thereof suitable for robot automation. The instructions include establishing a set of robot tasks, each defining at least a type of robot and a task objective, based on the portions of the job request that are suitable for robot automation and meet a first fleet objective. The instructions include applying fleet configuration services to the job content and the set of robot tasks to produce a fleet resource configuration data structure for the job request that associates at least one robot operating unit with each task in the set of tasks and robot adaptation instructions.