Robotic Trim Assembly Tracking for Moving Vehicle Bodies
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Solution Overview
Problem
Current robotic systems face challenges in accurately tracking and assembling vehicle components during final trim and assembly operations due to movement irregularities, vibrations, and varying lighting conditions, which hinder the repeatability and precision of robot motion control.
Innovation Solution
A neural network-based labeling system and unsupervised auto-labeling method that uses sensor fusion and feature-based tracking to robustly track objects, incorporating vision systems and force sensors to guide robot movement and calibrate sensors, ensuring accurate positioning and orientation of components despite environmental and mechanical uncertainties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If continuous motion of vehicles is used during FTA operations, then productivity is improved, but movement irregularities and vibrations occur that worsen tracking precision
Solution Approach 1:
The system continuously captures images of calibration features on the vehicle and uses computer vision algorithms to determine actual vehicle position and orientation. This feedback loop allows the robot controller to compensate for movement irregularities and vibrations in real-time, maintaining tracking precision despite continuous vehicle motion during FTA operations.
Solution Approach 2:
The system dynamically adjusts robot motion parameters based on measured vehicle position and orientation. By changing robot speed, acceleration, and positioning parameters in response to real-time vehicle state measurements, the system maintains assembly precision while accommodating continuous vehicle motion and vibrations.
2Measurement precision
If traditional computer vision matching algorithms are used, then object tracking is attempted, but varying lighting conditions and part color changes cause tracking loss
Solution Approach 1:
The system places specific calibration features with distinct visual characteristics at known locations on the vehicle. These localized features are designed to be easily distinguishable under varying lighting conditions, providing reliable tracking points that are not affected by general lighting changes or part color variations.
Solution Approach 2:
The system pre-defines the expected locations and characteristics of calibration features on the vehicle before assembly begins. This preliminary configuration allows the computer vision system to quickly identify and track these features regardless of environmental conditions, establishing a reliable reference framework in advance.
3Extent of automation
If robot motion control is implemented during vehicle movement, then automation is improved, but movement irregularities prevent accurate tracking
Solution Approach 1:
The system implements dynamic robot motion control that continuously adapts to changing vehicle position and orientation. The robot controller adjusts motion parameters in real-time based on feedback from vision systems tracking calibration features, enabling accurate robotic assembly operations despite vehicle movement and vibrations during automated FTA processes.
Data Source
AI summary
A robotic system for use in installing final trim and assembly part includes an auto-labeling system that combines images of a primary component, such as a vehicle, with those of computer based model, where feature based object tracking methods are used to compare the two. In some forms a camera can be mounted to a moveable robot, while in other the camera can be fixed in position relative to the robot. An artificial marker can be used in some forms. Robot movement tracking can also be used. A runtime operation can utilize a deep learning network to augment feature-based object tracking to aid in initializing a pose of the vehicle as well as an aid in restoring tracking if lost.