Vehicle Structural Component Machining With Image-Guided Cobot Planning
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Solution Overview
Problem
Current cobot systems in vehicle structure component processing, such as aircraft components, operate in a passive role and lack flexibility, relying heavily on human processor instructions due to insufficient image information for autonomous decision-making.
Innovation Solution
A cobot system with a manipulator, drive arrangement, and sensor arrangement for image information processing, utilizing image comparison with templates and processor data to generate optimized processing plans, enabling autonomous operation and adaptive processing actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If the cobot operates autonomously based on image information alone, then automation extent increases, but reliability decreases due to insufficient image information for autonomous decision-making
Solution Approach 1:
The patent combines multiple information sources (image information from sensor arrays, operator data from manual inspections, and processing plans) to create a comprehensive decision-making basis. The system control merges these diverse data types to generate reliable processing decisions, allowing the cobot to operate autonomously while maintaining high reliability through multi-source information integration.
Solution Approach 2:
The system control acts as an intermediary that processes and integrates image information with operator data. It mediates between the sensor array's automated capture and the final processing decisions, transforming raw image data into actionable insights by comparing against image templates and combining with manual inspection results.
2Reliability
If the cobot is directly controlled by the operator for all tasks, then reliability increases through human judgment, but productivity decreases due to manual operation limitations
Solution Approach 1:
The system dynamically adjusts the division of labor between operator and cobot based on task requirements and confidence levels. For routine tasks with high confidence in image recognition, the cobot operates autonomously. For complex or uncertain tasks, the system transitions to operator-controlled mode, creating a flexible hybrid workflow that optimizes both speed and accuracy.
Solution Approach 2:
The system implements feedback loops where operator interventions and manual inspection results are fed back into the system control. This feedback refines the image templates and improves the cobot's recognition accuracy over time, allowing progressively greater autonomy while maintaining reliability through continuous learning and adaptation.
3Measurement precision
If image processing complexity increases to improve recognition accuracy, then measurement precision increases, but device complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-generating image templates from CAD/CAM data and storing them in the system control. These templates represent expected target states and are prepared in advance, allowing the sensor array to compare actual images against known patterns rather than performing complex real-time analysis, thereby simplifying the processing system while maintaining high recognition accuracy.
Solution Approach 2:
The system creates simplified copies of the target state through image templates derived from CAD/CAM models. Instead of processing complex raw image data in real-time, the system compares sensor captures against pre-generated template copies, significantly reducing computational complexity while preserving measurement precision through accurate template matching.
4Adaptability or versatility
If the cobot performs learning runs with the operator to recognize objects, then adaptability increases, but loss of time increases due to training requirements
Solution Approach 1:
The system performs preliminary learning during setup phases before actual production begins. By establishing image templates and recognition patterns in advance through coordinated learning runs, the system prepares the cobot for autonomous operation, minimizing the time lost during productive manufacturing cycles while still achieving necessary adaptability.
Data Source
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AI summary
The invention relates to a method for machining at least one vehicle structural component (1) in a machining environment (2) using a cobot (3) in conjunction with a human operator (4), wherein the cobot (3) has a manipulator (5) with at least one end effector (6) for machining the vehicle structural component (1) and a drive arrangement (7) for moving the cobot (3), wherein a sensor arrangement (8) associated with the cobot (3) is provided for determining image information of the machining environment (1), wherein in a work routine the vehicle structural component is machined according to a machining plan (10) generated by means of a system controller (9) with at least one machining action (11).It is proposed that in a planning routine, image information (16) about at least one section of the vehicle structural component (1) is determined using the sensor arrangement (8), that the image information (16) is compared with a predefined image template (17) of a machined vehicle structural component (1) in an image comparison using the system control (9), and that the machining plan (10) for the vehicle structural component (1) is generated using the system control (9) according to a predefined planning rule based on the result of the image comparison and on the operator data of the human operator (4).