Camera-Assisted Robotic Arm Coordinate Calibration
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Solution Overview
Problem
Pre-programmed robots fail to perform tasks effectively when encountering unforeseen circumstances, limiting their use in fields requiring human intelligence and decision-making due to reliance on hard-coded position values that become obsolete with changes in the environment or device updates.
Innovation Solution
A camera-assisted robotic arm system that calibrates robotic movements by converting camera pixel locations to robotic arm coordinates, allowing for dynamic adaptation to changes in the mobile device interface and environment, reducing the need for hard-coded positions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pre-programmed robots use hard-coded position values to perform tasks, then they can execute repetitive actions with precision, but they fail when encountering unforeseen circumstances or environmental changes
Solution Approach 1:
The system captures images of the target device screen, detects the positions of UI elements, and uses this feedback information to dynamically adjust the robotic arm's movement coordinates. This closed-loop feedback mechanism allows the robot to adapt to changes in screen layout or device positioning, resolving the contradiction between reliable task execution and adaptability to environmental changes.
Solution Approach 2:
The patent replaces the traditional mechanical coordinate system (hard-coded positions) with an optical-based coordinate system derived from camera images. By using image processing to determine target positions rather than relying on pre-programmed mechanical coordinates, the system gains flexibility while maintaining precision in task execution.
2Productivity
If pre-programmed robots rely on hard-coded position values, then they can perform repetitive tasks efficiently, but the positions become obsolete with device updates or environmental changes
Solution Approach 1:
The system transitions from static hard-coded positions to dynamic coordinate determination. The robotic arm's target positions are continuously updated based on real-time image capture and processing, allowing the system to maintain high productivity while adapting to device updates or layout changes without reprogramming.
Solution Approach 2:
The system performs self-calibration by automatically capturing images, detecting UI element positions, and updating its own movement coordinates without external intervention. This self-service capability ensures continuous productivity while maintaining adaptability to environmental changes or device updates.
3Adaptability or versatility
If robots are equipped with camera-assisted dynamic coordinate determination, then they can adapt to changing environments, but the system complexity increases
Solution Approach 1:
The camera system serves multiple functions: it captures the target device screen for coordinate determination, verifies task completion, and provides visual feedback for system calibration. This multi-functionality reduces the need for separate specialized components, thereby limiting the increase in system complexity while maintaining high adaptability.
4Ease of manufacture
If pre-programmed robots use fixed coordinates, then the programming is simple and straightforward, but they cannot handle unforeseen circumstances or layout changes
Solution Approach 1:
The system automatically determines its own operational parameters by capturing images and processing coordinate information without requiring manual reprogramming. This self-service approach maintains programming simplicity while enabling the robot to handle unforeseen circumstances and layout changes through automatic adaptation.
Data Source
AI summary
Systems, methods, and computer-readable media are described for instructing a robotic arm to perform various actions on a touch-sensitive display screen of a mobile device based on captured images of the display screen. A camera can capture an image of the display screen while the mobile device is mounted in a mounting station with the display screen facing the camera, and the captured image can be analyzed to determine the relationship between the pixel locations in the image captured by the camera and the physical locations to which the robotic arm can be instructed to move. Based on the relationship, the system can instruct the robotic arm to touch and activate various on-screen objects displayed on the display screen of the mobile device based on the pixel locations of such on-screen objects in the images captured by the camera.