Robot Equipment Alignment with Vision-Based Reconfiguration
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Solution Overview
Problem
Conventional methods for aligning robots with equipment are laborious, costly, and inflexible, requiring manual intervention and reconfiguration for each task change, limiting a robot's ability to adapt to different applications and production schedules.
Innovation Solution
A two-stage alignment process using mechanical fixtures for initial coarse alignment and computer vision techniques to refine the alignment, allowing a robot to be easily reconfigured for multiple tasks without full reconfiguration, utilizing imaging sensors to detect alignment features and adjust positions based on image analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual alignment methods are used, then alignment precision can be achieved, but the process becomes laborious and costly
Solution Approach 1:
The patent replaces manual mechanical alignment procedures with an automated computer vision system. Imaging sensors capture images of alignment features on equipment, and image processing algorithms automatically determine positions and calculate alignment differences, eliminating the need for manual measurement and configuration processes while maintaining precision.
Solution Approach 2:
The alignment system performs self-alignment by automatically detecting alignment features, calculating positions, determining alignment differences, and configuring the robot without requiring manual intervention. The system serves itself by using imaging sensors and processing algorithms to complete the alignment task autonomously.
2Adaptability or versatility
If manual reconfiguration is performed for task changes, then the robot can adapt to new tasks, but downtime increases and productivity decreases
Solution Approach 1:
The patent implements preliminary alignment by establishing alignment features on equipment before the robot needs to perform tasks. These features remain in place, allowing the robot to quickly re-align using image processing when switching tasks, rather than requiring complete reconfiguration each time.
Solution Approach 2:
The system uses feedback from imaging sensors to continuously monitor and verify alignment. By capturing images of alignment features and processing them to determine current positions and alignment differences, the system can automatically adjust the robot's configuration for new tasks based on real-time visual feedback.
3Reliability
If full reconfiguration is performed for each task, then the robot is properly configured for the task, but time and resources are wasted
Solution Approach 1:
The patent segments the alignment process into two stages: initial alignment using mechanical fixtures and alignment features, and refinement alignment using image processing of those same features. This segmentation allows the system to maintain configuration accuracy while reducing the time required for reconfiguration by building upon the initial alignment rather than starting over.
Solution Approach 2:
The system recovers and reuses alignment features and initial alignment data when switching tasks. Instead of discarding the initial configuration and starting fresh, the system retains the alignment features on equipment and uses them as a foundation for rapid re-alignment, recovering valuable time and resources.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables a single robot to efficiently interface with various equipment by reducing alignment complexity and cost, facilitating adaptability to different tasks and reducing downtime through automated re-alignment processes.
Implementation Method 1
using computer vision techniques to facilitate aligning a robot to equipment so that the robot may interface with the equipment to perform a task
Data Source
AI summary
Computer vision techniques for configuring a robot having a robotic arm to interface with equipment to perform a task. The techniques include: capturing at least one image of the equipment: determining a position of a first alignment feature in the at least one captured image: determining, using the position of the first alignment feature in the at least one captured image, an alignment difference between a current alignment of the robot and the equipment with respect to a prior alignment of the robot and the equipment; and configuring the robot to interface with the equipment based on the alignment difference.