Robotic Coating System with 3D Model Segmentation
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Solution Overview
Problem
Current aircraft component painting processes are inefficient and pose health risks to human painters due to the physical demands and inaccuracies of pre-programmed robotic systems, which require human intervention for inspection and repainting.
Innovation Solution
A method and apparatus that generate a three-dimensional model of a group of objects using sensor data, forming segments to create precise instructions for a robotic coating system to apply coatings accurately, reducing human exposure and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If pre-programmed robotic painting systems are used, then human exposure to paint chemicals is reduced, but painting accuracy deteriorates requiring human inspection and repainting
Solution Approach 1:
The system uses sensors to capture real-time images of objects during the painting process, processes these images to determine coating quality, and provides feedback to the robotic system to adjust painting parameters. This closed-loop feedback mechanism ensures high painting accuracy while maintaining robotic operation, eliminating the need for human painters and their associated health risks.
2Manufacturing precision
If human painters are used, then painting accuracy is maintained, but physical demand and exhaustion increase
Solution Approach 1:
The robotic painting system is equipped with sensors and image processing capabilities that allow it to autonomously assess painting quality and adjust its own operation. The system serves itself by automatically detecting coating defects and correcting them without human intervention, thereby maintaining high accuracy while eliminating the physical burden on human painters.
3Productivity
If multiple objects are painted simultaneously, then productivity increases, but complexity of managing different object characteristics increases
Solution Approach 1:
The system segments the group of objects by analyzing their individual characteristics using sensors and image processing. Each object is identified and categorized based on its properties, allowing the robotic system to apply appropriate painting parameters to each segment. This segmentation approach enables efficient batch painting of multiple diverse objects while managing complexity through automated classification.
Solution Approach 2:
The robotic painting system dynamically adjusts its operation based on real-time sensor data from multiple objects. The system can modify painting parameters, speeds, and trajectories on-the-fly to accommodate different object characteristics within the same batch, enabling high productivity without requiring complex pre-programming for each object type.
Data Source
AI summary
A method and apparatus for coating a group of objects may be provided. A three-dimensional model of the group of objects may be generated. Segments that represent each object in the group of objects in the three-dimensional model may be formed. Instructions for coating the group of objects may be generated based on the segments. The instructions may be configured to cause a robotic coating system to coat the group of objects.