3D Scene Segmentation and Matching for Faster Robotic Task Planning
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Solution Overview
Problem
Robot-assisted operations face challenges in efficiently recognizing objects in real-world environments and planning interactions to accomplish specific tasks, as existing systems struggle with quick, reliable, and efficient object recognition and task execution.
Innovation Solution
A system comprising a data model with predefined associations of operations for robots, utilizing sensors to capture and process image data, and a processing module to match the data with pre-defined asset models, enabling efficient target recognition and operation planning, including pre-processing, transmission, and execution of operation plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional object recognition systems are used, then object recognition can be performed, but the recognition speed is slow and reliability is insufficient
Solution Approach 1:
The system performs preliminary actions by capturing multiple images of the target object from different viewpoints before recognition, and by pre-processing these images to extract features. This preliminary capture and pre-processing enables faster and more reliable recognition by having all necessary data ready in advance, resolving the contradiction between recognition speed and reliability.
Solution Approach 2:
The system transitions from 2D image analysis to 3D object recognition by capturing images from multiple viewpoints and constructing three-dimensional representations. This dimensional enhancement provides more comprehensive object information, improving both recognition reliability and enabling more accurate task planning while maintaining efficiency through automated processing.
2Manufacturing precision
If complex object interaction planning is implemented, then task execution accuracy improves, but system complexity increases
Solution Approach 1:
The system segments the complex task planning process into distinct modules: object recognition module, feature extraction module, task planning module, and execution module. Each module handles a specific aspect of the workflow, reducing overall system complexity while maintaining high task execution accuracy through specialized processing in each segment.
Solution Approach 2:
The system introduces an intermediary operation library that stores pre-defined operations and their parameters. This intermediary layer translates high-level task descriptions into specific executable operations, simplifying the planning process while ensuring accurate task execution by leveraging pre-validated operation templates.
Data Source
AI summary
A method and system, the method including receive image data representations of a set of images of a physical asset; receive a data model of at least one asset, the data model of each of the at least one assets including a semantic description of the respective modeled asset and at least one operation associated with the respective modeled asset; determine a match between the received image data and the data model of one of the at least one assets based on a correspondence therebetween; generate, for the data model determined to be a match with the received image data, an operation plan based on the at least one operation included in matched data model; execute, in response to the generation of the operation plan, the generated operation plan by the physical asset.