Robot Self-Diagnosis via Automated Image Acquisition
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Solution Overview
Problem
Current failure diagnosis systems for robots face challenges in determining the cause of failures due to external factors like distortion or part biting, requiring significant human intervention and resources.
Innovation Solution
A failure diagnosis support system that includes image acquisition means, control means for positioning and orienting the image acquisition device, part identifying means, and storage means to associate failed parts with optimal image acquisition positions and orientations, allowing for remote diagnosis and reduced human load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a person performs maintenance directly to determine the cause of failure, then the cause of failure can be easily determined, but it requires a large human load and constant human presence
Solution Approach 1:
The robot performs self-diagnosis by autonomously capturing images of itself using mounted image acquisition means. The control unit automatically controls the positioning and orientation of image acquisition means to face predetermined parts, enabling the system to diagnose its own failures without human intervention.
Solution Approach 2:
The patent replaces manual visual inspection with automated image acquisition and processing. Image acquisition means (cameras) mounted on the robot capture images of various parts, and the control unit processes these images to determine failure causes, substituting human mechanical inspection with automated optical and computational systems.
2Ease of operation
If image acquisition means is mounted on the robot for remote diagnosis, then human load is reduced, but it becomes difficult to determine causes of failure due to external factors like distortion or biting
Solution Approach 1:
The control unit dynamically controls the position and orientation of image acquisition means based on the robot's operational state. The system adjusts which parts are imaged and from what angles, enabling comprehensive coverage of potential failure points including areas affected by external factors like distortion or biting.
Solution Approach 2:
The diagnosis system divides the robot into multiple predetermined parts (arm, base, etc.) and assigns specific image acquisition strategies to each segment. The control unit selectively images individual parts based on where failures are most likely to occur or where external factors may have caused damage, enabling detailed examination of each segment.
3Reliability
If the image acquisition means is positioned to capture all parts of the robot, then comprehensive diagnosis is possible, but the device complexity increases
Solution Approach 1:
The image acquisition means serves multiple functions: capturing images of different robot parts (arm, base, etc.), detecting various types of failures (distortion, biting, sensor faults), and supporting comprehensive diagnosis. This multi-functionality reduces the need for separate specialized devices for each diagnostic task.
Solution Approach 2:
The control unit stores predetermined positions and orientations for image acquisition means before actual diagnosis occurs. During operation, the system simply retrieves and executes these pre-planned positioning instructions, avoiding the need for complex real-time calculations and reducing system complexity while maintaining comprehensive coverage.
Data Source
AI summary
A failure diagnosis support system includes first image acquisition means mounted on a robot for acquiring an image of the robot; and control means for controlling position and orientation of the first image acquisition means. The control means controls the position and orientation of the first image acquisition means at a predetermined timing so that the first image acquisition means faces a predetermined part of the robot. The first image acquisition means acquires an image of the predetermined part at the position and orientation controlled by the control means.