Collaborative Robot Health Monitoring via Task-Aware Test Programs
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Solution Overview
Problem
Current health evaluation methods for collaborative robots are inadequate for detailed diagnosis due to the diversity of task programs, making it difficult to ensure operation reliability and periodic testing.
Innovation Solution
A method and system for health monitoring of collaborative robots that perform real-time or periodic health monitoring based on sensing data from programs tailored to each component, ensuring operation time and periodicity by calling test programs at specific conditions and analyzing acquired data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a fixed analysis technique is used for health evaluation, then the evaluation process is simplified, but it becomes difficult to handle the diversity of task programs and achieve accurate health assessment
Solution Approach 1:
The patent implements dynamic health evaluation by automatically selecting analysis techniques based on the detected task program type. The system transitions from a fixed analysis approach to a dynamic one that adapts to different task programs, thereby maintaining evaluation simplicity while improving assessment accuracy for diverse robotic tasks.
Solution Approach 2:
The system changes the analysis parameters based on the task program being executed. By detecting the task program type and selecting corresponding analysis parameters, the system achieves accurate health assessment across different task scenarios without requiring manual configuration for each program type.
2Ease of operation
If a tester performs heuristic health evaluation or uses a fixed action program, then the overall state of the collaborative robot can be diagnosed, but detailed diagnosis of each component is not possible
Solution Approach 1:
The patent segments the health evaluation process into task program detection and component-specific analysis. By dividing the evaluation into overall robot state assessment and detailed component diagnosis based on detected task programs, the system achieves both ease of operation and precise component-level insights.
Solution Approach 2:
The system implements a universal health evaluation framework that handles multiple task programs through a single detection and selection mechanism. This multi-functional approach allows the same system to perform both overall state diagnosis and detailed component analysis across diverse robotic tasks.
3Reliability
If statistical analysis of sensing data is performed for health evaluation, then health degradation can be identified, but it is difficult to guarantee operation reliability and periodic testing due to complicated data patterns from diverse task programs
Solution Approach 1:
The system performs preliminary detection of task programs before conducting statistical analysis. By pre-identifying the task program type and selecting appropriate analysis methods in advance, the system simplifies the data analysis process while maintaining reliable health monitoring, avoiding the complexity of handling diverse data patterns without pre-processing.
Data Source
AI summary
A method and system for health monitoring of a collaborative robot are provided. The method includes calling a test program installed in a collaborative robot for health monitoring of the collaborative robot when the collaborative robot satisfies a call condition of the test program, performing a test by operating the collaborative robot based on the test program, and collecting and analyzing a result of the test by the collaborative robot.