Robot Operation Progress Tracking With Segmented ML Control
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Solution Overview
Problem
Conventional robot control systems lack the ability to provide a detailed understanding of operation progress, making it difficult to assess the robot's performance accurately.
Innovation Solution
A robot control device equipped with a trained model that utilizes machine learning to predict and output a progress degree for each operation, dividing the series of operations into process operations and associating a changing range of progress degrees with these operations, allowing for a detailed understanding of the robot's progress.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If the progress degree changes with the base trained model as a unit, then the control system is simple, but it is not suitable for detailed understanding of the progress
Solution Approach 1:
The patent segments the series of operations into multiple process operations, and further segments each process operation into multiple phases. The progress degree is then calculated at each phase level, providing detailed progress information without requiring complete reconfiguration of the control system. This hierarchical segmentation allows detailed tracking while maintaining overall system simplicity.
2Loss of information
If the series of operations is divided into multiple process operations, then detailed progress understanding is achieved, but the model complexity increases
Solution Approach 1:
The patent performs preliminary segmentation of the series of operations into process operations and phases during the model construction phase. Work data are pre-organized with corresponding progress degree ranges assigned to each phase. This preliminary structuring allows the trained model to efficiently query and calculate progress without complex real-time computations, reducing operational complexity while maintaining detailed progress tracking capability.
Data Source
AI summary
A robot control device includes a trained model and a progress degree acquirer. The trained model is trained on input data and output data for a series of operations to be performed by a robot. The trained model determines into which of multiple process operations corresponding to a result of a division of the series of operations, the input data are classified. In the trained model, an output transition, being a temporal transition of output to realize the process operation, is defined in association with each classification. The progress degree acquirer acquires a progress degree indicating to which degree of progress of the series of operations the output data output by the trained model in response to input of the input data correspond. The progress degree varies depending on an ordinal number of output corresponding to the input data in the output transition associated with the process operation, in a progress degree range corresponding to the process operation that is a result of the classification of the input data performed by the trained model.