Vision-Guided Workpiece Transfer for Flexible Pick-and-Place
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Solution Overview
Problem
Existing automated workpiece transfer systems are costly and time-consuming to deploy due to customization requirements and have limited processing rates, as they often require significant manual reconfiguration to handle different workpieces and orientations.
Innovation Solution
An automated system utilizing an imaging device and a machine-learning model to identify pickable workpieces, defining operating parameters for an autonomous pick-and-place robot to retrieve and transfer workpieces based on position and orientation, allowing for efficient handling of varied workpieces without extensive reconfiguration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional automated workpiece transfer systems are used, then workpiece transfer can be automated, but the system is costly and time-consuming to deploy due to customization requirements
Solution Approach 1:
The system employs a universal robotic arm with interchangeable end effectors that can handle multiple workpiece types and orientations without requiring custom-built transfer mechanisms for each application, thereby reducing deployment costs and time while maintaining full automation capability
Solution Approach 2:
The patent replaces traditional mechanical transfer systems with vision-guided robotic manipulation, where machine learning models process imaging data to determine workpiece positions and orientations, and robotic arms execute transfer operations based on software control rather than hardwired mechanical linkages
2Productivity
If traditional feeders are used to transfer workpieces, then workpieces can be sorted and fed to assembly lines, but the processing rate is limited due to manual reconfiguration requirements
Solution Approach 1:
The system enables robots to autonomously adapt to different workpieces by capturing images, processing them through machine learning models to identify positions and orientations, and automatically adjusting transfer parameters without requiring manual reconfiguration, thereby maintaining high processing rates while eliminating manual intervention
Solution Approach 2:
The patent dynamically adjusts operational parameters such as robotic arm trajectories, end effector positions, and transfer timing based on real-time image analysis and machine learning predictions, allowing the system to optimize processing rates for each workpiece configuration without physical reconfiguration
Data Source
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AI summary
Automated workpiece transfer systems and methods of implementing thereof are disclosed. The system can include an imaging device operable to capture an initial image of workpieces loaded onto a loading area; an autonomous pick-and-place robot, and a processor in communication with the imaging device and the pick-and-place robot. The autonomous pick-and-place robot can include an end-of-arm-tooling component operable to retrieve pickable workpieces from the loading area and transfer the pickable workpieces to a receiving area according to a set of operating parameters. The processor can be operable to apply a machine-learning model to the initial image to identify pickable workpieces; identify a region of interest within the initial image; and based on the initial image, define the set of operating parameters for operating the end-of-arm-tooling component to retrieve the one or more pickable workpieces.