Robot Arm Control Apparatus for Teaching Adaptation
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Solution Overview
Problem
Household robots face inefficiencies in teaching and adapting to environmental changes, requiring extensive manual intervention and programming, which is laborious and prone to errors due to variations in household environments.
Innovation Solution
A control apparatus and method for a robot arm that includes a motion information acquiring unit, correction motion information acquiring unit, environment information acquiring unit, position control unit, motion correction unit, and control rule generating unit, allowing for automatic operation based on learned motion and environmental changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional teaching methods are used where a teaching person manually guides the robot to all teaching points, then the robot can be taught the required motion, but the teaching process becomes extremely time-consuming and laborious
Solution Approach 1:
The robot performs self-teaching by autonomously moving to teaching points and capturing images without human intervention. The image processing unit automatically identifies teaching point positions from captured images, eliminating the need for manual guidance while maintaining teaching accuracy.
Solution Approach 2:
The patent replaces the mechanical manual guidance system with an automated vision-based system. Instead of a teaching person physically guiding the robot, the robot uses its imaging device to capture images and the image processing unit to automatically determine teaching point positions, substituting mechanical interaction with optical and computational processes.
2Reliability
If the entire motion must be re-taught from the beginning when any part of the taught motion needs modification, then the motion can be corrected, but the process becomes inefficient
Solution Approach 1:
The motion teaching process is segmented into independent teaching points rather than a continuous motion sequence. Each teaching point can be independently identified, modified, or added through image processing, allowing selective correction without re-teaching the entire motion path.
Solution Approach 2:
The robot captures images at teaching points in advance during the teaching phase. These pre-captured images are stored and can be processed later to identify and modify specific teaching point positions without requiring re-execution of the entire motion sequence.
3Adaptability or versatility
If parallel use with programming through a teaching pendant is employed, then motion modification is possible, but the manipulation steps increase and programming language learning is required
Solution Approach 1:
The system performs automatic image processing and teaching point identification without requiring operator intervention for programming. The robot autonomously processes captured images to determine teaching point positions, eliminating the need for operators to learn programming languages or use complex teaching pendants.
Solution Approach 2:
The patent replaces the teaching pendant interface with an automated image processing system. Instead of requiring operators to manually input commands through a teaching pendant, the system uses optical capture and computational image analysis to automatically determine and modify motion parameters.
4Reliability
If the robot executes the taught task exactly as taught, then the programmed motion is precisely followed, but the robot may stop or erroneously perform the task when environment variations occur
Solution Approach 1:
The robot uses feedback from captured images to verify the current environment matches the taught environment. By comparing real-time image data with stored teaching point images, the system can detect environmental variations and adjust its motion execution accordingly, preventing erroneous stops or actions.
Solution Approach 2:
The system dynamically adjusts motion execution based on environmental conditions. Instead of rigidly following pre-programmed positions, the robot uses real-time image processing to identify actual teaching point locations and adapts its motion path to accommodate environmental variations while maintaining task accuracy.
Data Source
AI summary
Motion information of a robot arm stored in a motion information database is acquired. A person manipulates the robot arm, and correction motion information at the time of the motion correction is acquired. An acquiring unit acquires environment information. A motion correction unit corrects the motion information while the robot arm is in motion. A control rule generating unit generates a control rule for allowing the robot arm to automatically operate based on the corrected motion information and the acquired environment information. The motion of the robot arm is controlled based on the generated control rule.