AR Robot Diagnosis Guide for Faster Industrial Troubleshooting
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Solution Overview
Problem
Industrial robots present challenges in fast and efficient troubleshooting due to poorly documented and inaccessible troubleshooting documentation, complexity of controller and robot hardware, and the need for expertise, leading to time-consuming and costly diagnostic processes.
Innovation Solution
An augmented reality system that communicates with an industrial robot's controller to collect data, identify an appropriate diagnosis decision tree, and provide an interactive step-by-step troubleshooting guide on a mobile device, including augmented reality for depicting actions during testing and component replacement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional troubleshooting documentation is used, then comprehensive diagnostic information is available, but accessibility and ease of use deteriorate due to scattered manuals and poor documentation
Solution Approach 1:
The patent consolidates scattered troubleshooting documentation, decision trees, and diagnostic information into a single integrated mobile application. This merging of previously distributed resources into one accessible platform resolves the contradiction by making comprehensive information easily obtainable without requiring users to search through multiple manuals.
Solution Approach 2:
The mobile device acts as an intermediary between the robot controller and the user, providing a user-friendly interface that translates complex diagnostic data into actionable guidance. This intermediary layer resolves the contradiction by making technical information accessible to users without requiring deep expertise.
2Measurement precision
If comprehensive troubleshooting documentation is provided, then diagnostic accuracy improves, but complexity increases making instructions difficult to execute
Solution Approach 1:
The patent segments comprehensive diagnostic information into structured decision trees with discrete, manageable steps. Each decision point presents limited choices rather than overwhelming users with all possible diagnostic paths simultaneously. This segmentation maintains diagnostic accuracy while reducing perceived complexity by breaking down the troubleshooting process into sequential, easy-to-follow segments.
3Reliability
If all possible root causes are investigated, then diagnostic completeness improves, but time and cost increase significantly
Solution Approach 1:
The system performs preliminary actions by automatically collecting robot controller data and pre-processing diagnostic information before presenting it to the user. Decision trees are pre-structured based on common failure modes, allowing the system to quickly eliminate unlikely causes and focus on probable issues. This preliminary preparation maintains diagnostic completeness while significantly reducing the time users need to spend on troubleshooting.
4Measurement precision
If detailed troubleshooting instructions are provided, then diagnostic accuracy improves, but ease of operation deteriorates for users without expertise
Solution Approach 1:
The mobile application serves as an expert intermediary that translates complex diagnostic logic into simple, guided instructions for non-expert users. The system handles the complexity of interpreting robot controller data and navigating decision trees, while presenting users with straightforward yes/no questions and clear actionable steps. This intermediary function maintains diagnostic accuracy while making the process accessible to users without specialized knowledge.
Data Source
AI summary
An augmented reality (AR) system for diagnosis, troubleshooting and repair of industrial robots. The disclosed diagnosis guide system communicates with a controller of an industrial robot and collects data from the robot controller, including a trouble code identifying a problem with the robot. The system then identifies an appropriate diagnosis decision tree based on the collected data, and provides an interactive step-by-step troubleshooting guide to a user on an AR-capable mobile device, including augmented reality for depicting actions to be taken during testing and component replacement. The system includes data collector, tree generator and guide generator modules, and builds the decision tree and the diagnosis guide using a stored library of diagnosis trees, decisions and diagnosis steps, along with the associated AR data.