Mobile Robot Workcell Self-Assembly for Complex Workflow Scaling
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Solution Overview
Problem
Existing approaches to organizing and executing workflows in laboratory settings, particularly those involving robotic equipment, face challenges in quickly and seamlessly assembling workcells, adapting equipment for multiple uses, and optimizing the integration of multiple instruments across various locations, leading to limitations in scalability and efficiency.
Innovation Solution
A system and method for self-assembly of workcells using mobile robotic platforms, which models and simulates configuration schemes and pathway topologies to automate the deployment and transportation of workcells, enabling efficient execution of workflows by analyzing spatial and temporal requirements and constraints, and allocating available resources dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional approaches are used to organize and execute workflows involving robotic equipment, then high throughput can be achieved, but difficulties persist in quickly and seamlessly organizing workcells, particularly for time-sensitive or urgent tasks
Solution Approach 1:
The system pre-defines multiple configuration schemes for workcells that can be rapidly deployed when needed. These pre-configured schemes include predefined robot paths, tool configurations, and workflow parameters that can be activated immediately for time-sensitive tasks, eliminating the need for time-consuming workcell organization at the moment of execution.
Solution Approach 2:
The system dynamically selects and switches between different configuration schemes based on the specific workflow requirements and time constraints. This dynamic adaptability allows the workcell to be reconfigured on-the-fly for different tasks, maintaining high throughput while enabling rapid response to urgent requirements through automated scheme selection and deployment.
2Ease of operation
If robotic equipment is constrained to a table or workspace, then ease of operation is improved, but adaptability for other uses and scalability to multiple locations is limited
Solution Approach 1:
The system creates universal workcell configuration schemes that can be deployed across multiple locations and adapted to different workflows. Each configuration scheme is designed to be location-agnostic and task-adaptable, allowing the same robotic equipment to serve multiple functions and locations through software-based reconfiguration rather than physical repositioning, thereby enhancing versatility while maintaining operational simplicity.
Solution Approach 2:
The system uses virtual copying of workcell configurations through digital models and simulation. Configuration schemes can be replicated and deployed to multiple locations without physically moving the actual robotic equipment, enabling scalability across different workspaces while maintaining consistent operational parameters and ease of use through standardized virtual templates.
3Device complexity
If existing approaches are used to integrate multiple instruments at multiple locations, then device complexity is reduced, but scalability and optimized integration of instruments are limited
Solution Approach 1:
The system segments the overall workflow into multiple independent configuration schemes, each representing a specific workflow scenario. This segmentation allows individual schemes to be optimized independently for their specific purposes while maintaining overall system coherence. Each scheme can be scaled and deployed independently, enabling progressive system expansion without increasing overall complexity, as each segment remains manageable and well-defined.
Solution Approach 2:
The system uses parameter-based configuration schemes that can be adjusted and optimized for different locations and instrument combinations. By changing parameters such as robot paths, tool offsets, and workflow timings within the same configuration framework, the system achieves scalable integration across multiple locations without requiring fundamentally different system architectures, thereby maintaining low complexity while enhancing productivity and scalability.
4Adaptability or versatility
If manual organization of workcells is used, then adaptability to changing needs is improved, but productivity and efficiency are reduced
Solution Approach 1:
The system implements self-service through automated configuration scheme selection and deployment. When a workflow requirement changes, the system automatically selects the appropriate pre-defined configuration scheme and deploys it without manual intervention. This self-service capability maintains high adaptability to changing needs while preserving productivity, as the automated system handles the reconfiguration process that would otherwise require manual effort, thereby eliminating the trade-off between adaptability and efficiency.
Data Source
AI summary
A framework for self-assembly of workcells includes analyzing multiple constraints to determine configuration and movement of mobile robots and/or one or more robotic devices in the performance of workflows. The framework includes multiple elements that model a deployment and transportation strategy in a rules engine for identifying and allocating available workcell resources, and generates configuration schemes and pathway topologies that govern the self-assembly of workcells and execution of workflows using such workcells. The framework is applicable to single or multi-robot configurations, and enables self-assembly of each workcell in a single path or in multiple paths. The framework is further applicable to multiple item processing scenarios, such as round robin processing, batch processing, or both, and accounts for multiple handling and processing actions. The framework further includes a machine learning engine for additional processing of workcell and workflow constraints to improve deployment and transportation outcomes.