Collaborative Robot Benchmark Coordinates for Sensor Drift Correction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Collaborative robots face challenges in maintaining accurate operation due to baseline shift and zero-drift issues in displacement sensors, affecting their efficiency and reliability.
Innovation Solution
An Industrial Internet of Things (IoT) system is developed to determine benchmark coordinates of collaborative robots, utilizing a service platform, management platform, and sensing network platform to select key moments, establish collaborative robot spaces, and compare image information and displacement sensor data to monitor and adjust the robot's operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If displacement sensor is used to monitor collaborative robot operation, then operation monitoring capability is improved, but baseline shift and zero-drift occur after long-term operation affecting accuracy
Solution Approach 1:
The patent introduces image information (visual data) as an intermediary to verify and correct the displacement sensor data. The image processing system captures the collaborative robot's position independently, and this visual information serves as a mediator to identify and correct baseline shifts and zero-drift in the displacement sensor, thereby maintaining measurement precision while preserving continuous monitoring capability.
Solution Approach 2:
The system implements feedback by comparing displacement sensor coordinates with image-derived coordinates at key moments. When deviations are detected (indicating baseline shift or zero-drift), the system uses the image information as reference to correct the displacement sensor data, creating a closed-loop feedback mechanism that maintains accuracy over long-term operation.
2Duration of action of moving object
If displacement sensor data is used for monitoring, then continuous operation data is obtained, but baseline shift causes data accuracy degradation over time
Solution Approach 1:
The system performs preliminary verification by capturing image information at key moments during the collaborative robot's operation. This visual data is processed in advance to establish reference positions, which are then used to correct displacement sensor data before baseline shift significantly degrades accuracy, enabling long-term continuous monitoring with maintained precision.
Solution Approach 2:
The system implements feedback by comparing displacement sensor coordinates with image-derived coordinates at key moments. When deviations are detected (indicating baseline shift or zero-drift), the system uses the image information as reference to correct the displacement sensor data, creating a closed-loop feedback mechanism that maintains accuracy over long-term operation.
3Reliability
If image information is used to obtain coordinates, then visual verification capability is improved, but data processing complexity increases
Solution Approach 1:
The system applies partial action by processing image information only at key moments when the collaborative robot is stationary, rather than continuously. This selective approach provides visual verification capability when most needed (for baseline verification) while significantly reducing computational complexity compared to continuous image processing.
Solution Approach 2:
The patent segments the monitoring process into discrete key moments within the collaborative robot's processing cycle. By dividing continuous monitoring into specific stationary phases where image capture occurs, the system achieves visual verification without the computational burden of continuous image processing, as images are only captured and processed at these segmented intervals.
4Productivity
If key moments are selected for monitoring, then monitoring efficiency is improved, but coverage of operational states is reduced
Solution Approach 1:
The system performs preliminary identification of key moments based on the collaborative robot's processing cycle characteristics. By pre-determining when the robot is stationary and most suitable for image capture, the system ensures that monitoring occurs at optimal moments without missing critical operational states, balancing efficiency with comprehensive coverage.
Data Source
AI summary
Disclosed is an IoT system for determining a work situation of a collaborative robot, comprising: a service platform, a management platform, and a sensing network platform. The management platform includes a selection module configured to select at least one key moment from a processing cycle of a target collaborative robot; a space module configured to establish a collaborative robot space and a key point of the collaborative robot; a benchmark module configured to take coordinates of the key point of the target collaborative robot in the collaborative robot space at the key moment in a standard processing situation as benchmark coordinates; an acquisition module configured to obtain first coordinates and second coordinates; and a calculation module configured to determine a confidence of each key moment; adjust the key moment; and monitor the work situation of the target collaborative robot, and send the work situation to a user platform.