Robotic Wire Contact Pose Estimation via Edge Detection
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Solution Overview
Problem
Current methods for automating the assembly of wire bundles in aircraft manufacturing lack precision in inserting wire contacts into connectors, particularly when dealing with varying wire shapes, colors, and reflectivity, and require complex machine learning models for accurate pose determination.
Innovation Solution
A robotic system equipped with a camera system and controller that generates a sequence of images of the wire contact as it moves, detects edges, removes background edges, and identifies the wire contact to determine its pose without prior modeling, allowing for precise insertion into connectors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are used to determine wire contact pose, then accuracy in inserting wire contacts into connectors is improved, but device complexity increases
Solution Approach 1:
The patent replaces complex machine learning models with a simplified geometric modeling approach. Instead of using trained neural networks to determine wire contact pose, the system uses analytical geometry methods with pre-defined wire contact models, achieving high precision while significantly reducing computational complexity and processing time.
Solution Approach 2:
The patent creates simplified geometric copies or representations of wire contacts with known parameters. By using pre-defined geometric models that replicate the essential features of wire contacts, the system can determine pose through geometric calculations rather than complex learning models, maintaining accuracy while reducing complexity.
2Adaptability or versatility
If machine learning models are trained for various connector types and wire insulation settings, then adaptability is improved, but loss of time in training and deployment increases
Solution Approach 1:
The patent implements a universal geometric modeling framework that can handle various connector types and wire insulation settings without requiring separate training for each case. The analytical approach uses general geometric principles that apply across different scenarios, providing adaptability while eliminating training time requirements.
Solution Approach 2:
The system achieves adaptability by changing geometric parameters in the pre-defined models rather than retraining machine learning models. By adjusting wire contact dimensions, connector geometries, and material properties in the analytical models, the system adapts to different connector types and wire settings instantly without time loss.
Data Source
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AI summary
A method, apparatus, system, and computer program product for positions a wire contact (138, 224, 342, 400). A sequence of images (232, 300) of a wire contact (138, 224, 342, 400) is generated while the wire contact (138, 224, 342, 400) moves from a first position (236) to a second position (238). The sequence of images (232, 300) is generated by the camera system (209) connected to the end effector (136, 208, 400) and the wire contact (138, 224, 342, 400) is held by the end effector (136, 208, 400). Edges (240) are detected in the sequence of images (232, 300) to form edge images (142, 241, 320). Background edges (242) are removed from the edges (240) in the edge images (142, 241, 320) leaving contact edges (244) in the edges (240) for the wire contact (138, 224, 342, 400) to form a contact edge image (246, 330). The wire contact (138, 224, 342, 400) is identified using the contact edges (244) in the contact edge image (246, 330). A pose (150, 226) of the wire contact (138, 224, 342, 400) is determined from the wire contact (138, 224, 342, 400) identified in the contact edge image (246, 330).