Dual-Resolution 3D Vision for Robotic Arm Positioning
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Solution Overview
Problem
Existing 3D computer-vision systems for robotic applications face challenges in providing variable spatial resolution, which is essential for accurately guiding robotic arms in handling both tiny and larger components, as high-resolution systems are inadequate for larger components and low-resolution systems lack the precision needed for tiny components.
Innovation Solution
A dual-resolution 3D computer-vision system comprising a low-resolution 3D camera module with a wide field of view and a high-resolution 3D camera module, where the high-resolution module's field of view is located within the low-resolution module's field of view, allowing for simultaneous visualization of components at two different resolutions, along with an error-compensation module using machine-learning to correct pose errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a high-resolution 3D camera module is used, then measurement precision for tiny components is improved, but the field of view is limited and cannot accommodate larger components
Solution Approach 1:
The system divides the visual monitoring task into two segments: a low-resolution 3D camera module handles large-area visualization, while a high-resolution 3D camera module handles detailed measurement. This segmentation allows each module to operate within its optimal performance range without compromise.
Solution Approach 2:
The high-resolution camera module's field of view is nested within the low-resolution camera module's field of view. This nested arrangement enables the high-resolution module to capture detailed views of specific regions while the low-resolution module provides the broader contextual view, effectively combining both large area coverage and high measurement precision.
2Area of stationary object
If a low-resolution 3D camera module with wide field of view is used, then the ability to handle larger components is improved, but measurement precision for tiny components deteriorates
Solution Approach 1:
The system assigns different resolution tasks to separate camera modules: the low-resolution module is dedicated to capturing wide-area views for large components, while the high-resolution module is dedicated to capturing detailed views for precise measurements of tiny components.
Solution Approach 2:
The system uses an image processing module as an intermediary that receives images from both camera modules, aligns them based on coordinate transformation, and fuses them into a composite image. This intermediary processing enables the system to leverage the strengths of both low-resolution (wide view) and high-resolution (detailed measurement) cameras.
3Adaptability or versatility
If a dual-resolution system with multiple camera modules is implemented, then adaptability to handle both tiny and large components is improved, but device complexity increases
Solution Approach 1:
The system creates a multi-functional vision system where the low-resolution camera module serves wide-area monitoring functions and the high-resolution camera module serves detailed measurement functions. This universal design allows a single system to handle both large and tiny components effectively.
Solution Approach 2:
The system replaces complex mechanical solutions (such as physically changing camera lenses or switching between different cameras) with computational methods including coordinate transformation, image alignment, and fusion algorithms. This substitution reduces mechanical complexity while maintaining adaptability.
4Productivity
If variable spatial resolution is achieved through multiple camera modules, then productivity for diverse manufacturing tasks is improved, but calibration and coordination complexity increases
Solution Approach 1:
The system implements self-calibration capabilities where the coordinate transformation parameters between the low-resolution and high-resolution camera modules are automatically determined through image fusion algorithms. This self-service approach reduces the need for manual calibration and accelerates system setup.
Solution Approach 2:
The system uses feedback mechanisms where the image processing module continuously adjusts the alignment and fusion of images from both camera modules based on detected features and coordinate transformations. This feedback loop ensures accurate coordination between the two camera modules during actual operation.
Data Source
AI summary
One embodiment can provide a robotic system. The robotic system can include a robotic arm comprising an end-effector, a robotic controller configured to control movements of the robotic arm, and a dual-resolution computer-vision system. The dual-resolution computer-vision system can include a low-resolution three-dimensional (3D) camera module and a high-resolution 3D camera module. The low-resolution 3D camera module and the high-resolution 3D camera module can be arranged in such a way that a viewing region of the high-resolution 3D camera module is located inside a viewing region of the low-resolution 3D camera module, thereby allowing the dual-resolution computer-vision system to provide 3D visual information associated with the end-effector in two different resolutions when at least a portion of the end-effector enters the viewing region of the high-resolution camera module.