Robot vision super visor for hybrid homing, positioning and workspace UFO detection enabling industrial robot use for consumer applications
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Solution Overview
Problem
Industrial robots used in home or restaurant settings require frequent maintenance and calibration due to collisions with unidentified foreign objects, leading to costly service technician visits and operational inefficiencies, as they lack the ability to self-correct and recalibrate without expert intervention.
Innovation Solution
A robot vision supervisor system utilizing primary and secondary orthogonal cameras for image processing to guide the robot back to its home position, detect position errors, and identify unidentified foreign objects, eliminating the need for manual technician guidance by computing and verifying the robot's positioning accuracy through image comparison and alarm notifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If industrial robots are deployed in home consumer settings, then they can perform tasks in uncontrolled environments, but they frequently collide with unidentified foreign objects causing calibration loss and requiring service technician intervention
Solution Approach 1:
The robot system performs self-diagnosis and self-correction by using vision sensors to detect calibration loss indicators (such as misaligned homing markers or unexpected objects) and automatically executes recalibration routines without requiring service technician intervention, enabling the system to service itself
Solution Approach 2:
The vision supervisor continuously monitors the workspace and robot position, providing real-time feedback about calibration status by detecting visual indicators such as homing marker positions or unexpected objects, allowing the system to detect and correct calibration drift before it causes operational failures
2Manufacturing precision
If manual homing and recalibration procedures are used, then positioning accuracy can be restored after collisions, but skilled technicians must be summoned for each failure event increasing operational costs
Solution Approach 1:
The robot autonomously performs recalibration by detecting visual indicators of position loss and automatically executing homing sequences or coordinate system corrections without human intervention, restoring positioning accuracy independently
Solution Approach 2:
The patent replaces manual mechanical homing procedures with vision-based automatic homing, where cameras and image processing algorithms detect homing markers and compute robot position automatically, substituting the need for technician manual guidance
3Measurement precision
If traditional homing sensors are used, then robot axes can be calibrated to home positions, but the system cannot detect or prevent collisions with foreign objects in the workspace
Solution Approach 1:
The vision supervisor acts as an intermediary between the robot and its environment, using visual sensors to detect foreign objects and homing markers, providing information that prevents collisions and enables automatic homing without direct mechanical contact or manual intervention
Solution Approach 2:
The system performs preliminary detection of foreign objects and homing marker positions before the robot executes motion commands, preventing collisions in advance and preparing calibration data beforehand to enable rapid automatic recalibration when needed
Data Source
AI summary
A robot vision supervisor system for use with industrial robots being placed with average home consumers or at restaurants that is capable of guiding the robot to its homing position for initial homing or when a loss of reference has occurred. Further the robot vision supervisor is able to detect presence of unidentified foreign objects in the workspace and guide the robot to navigate around and prevent collisions. Image processing algorithms are used with primary, orthogonal and cross camera arrays to increase reliability and accuracy of the robot vision supervisor. Lookup tables of images are captured and stored by controller for discretized positions in the full robot workspace when robot is in known good calibration to compare with images during regular operation for detecting anomalies.