Mixed Reality Workflow Mapping Across Different Equipment Layouts
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Solution Overview
Problem
Existing mixed reality (MR) workflows are time-consuming to generate and require manual recapture when changes occur in process equipment or layout, limiting their adaptability to different locations and layouts.
Innovation Solution
A method that uses non-spatial characteristics of objects to map MR workflows from one location to another, allowing dynamic adaptation without requiring identical layouts, by identifying task-specific capabilities and environmental constraints to generate a mapped workflow.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual recapture method is used to generate MR workflows, then the workflow accuracy is improved, but the time consumption and effort increase significantly
Solution Approach 1:
The system creates a digital copy of the workflow from the first location and maps it to the second location using non-spatial characteristics. This copying approach eliminates the need for manual recapture while maintaining workflow accuracy, as the mapped workflow preserves task sequences and object relationships from the original location.
Solution Approach 2:
The system changes the parameter basis for workflow generation from spatial coordinates to non-spatial characteristics. By using task-specific capabilities and environmental constraints as mapping parameters instead of physical layouts, the system enables rapid adaptation to different locations without requiring identical equipment arrangements.
2Stability of the object's composition
If spatial mapping method is used to adapt MR workflows to different locations, then the layout consistency is improved, but the adaptability to different equipment configurations decreases
Solution Approach 1:
The system uses non-spatial characteristics such as task-specific capabilities and environmental constraints that are universal across different locations and equipment configurations. These characteristics enable the same workflow to be adapted to various layouts and equipment types without requiring spatial reconfiguration, making the mapping process universally applicable.
Solution Approach 2:
The system segments the workflow into discrete tasks and objects, then maps them based on their non-spatial characteristics rather than spatial relationships. This segmentation allows each task-object pair to be independently mapped to corresponding elements in the second location, enabling flexibility in equipment arrangements while maintaining workflow integrity.
3Reliability
If traditional MR workflow generation is used, then the workflow reliability is improved, but the productivity and update speed decrease
Solution Approach 1:
The system performs preliminary analysis of non-spatial characteristics during the initial workflow capture at the first location. By pre-processing and storing task-specific capabilities and environmental constraints, the system enables rapid mapping to the second location without requiring time-consuming manual recapture, thus improving productivity while maintaining reliability.
Solution Approach 2:
The system replaces the manual mechanical process of workflow recapture with an automated computational mapping process. By using algorithms to match non-spatial characteristics between locations, the system substitutes human effort with automated processing, significantly increasing workflow generation speed and update frequency.
Data Source
AI summary
A computer-implemented method of generating a mixed reality workflow is described. The method comprises identifying a series of tasks and generating an input task-to-object-mapping by analyzing data that defines a process performed by a first user interacting with objects in a first location. The input task-to-object-mapping that maps each task from the series of tasks to an object used in the respective task. A task-specific non-spatial characteristic of each object in the input task-to-object-mapping is determined and used to map each object in the input task-to-object-mapping to a candidate object identified at a second location to generate an output task-to-object-mapping. The series of tasks, location data defining a position of each candidate object in the second location and output task-to-object-mapping are used to generate a mapped workflow which is then output to a device in the second location.


