Robot Behavior Snapshots for Real-Time Fault Diagnosis
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Solution Overview
Problem
Existing robotics systems face challenges in providing live, targeted, and contextual diagnostic data for real-time debugging and fault fixing in complex environments with multiple robots, leading to inefficiencies in identifying and resolving issues due to overwhelming data streams and lack of granular analysis capabilities.
Innovation Solution
A system that generates and displays targeted information by creating snapshots of robot behaviors, aggregating and reporting them in real-time, allowing for live debugging and error diagnosis, and enabling collaboration between robots, with features like rosbag and log collection for real-time error analysis and visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive data collection from multiple robots is implemented, then diagnostic accuracy is improved, but data processing complexity increases
Solution Approach 1:
The system segments the comprehensive data stream into discrete, structured snapshots that capture specific behavioral contexts. Each snapshot divides the complex data into manageable units with defined fields (timestamps, robot IDs, behavior types, parameters), making the data processable while retaining diagnostic value.
Solution Approach 2:
The patent introduces an intermediary processing layer that acts as a mediator between raw robot data and diagnostic analysis. This layer standardizes and structures the data into a common format, filtering and organizing information before it reaches the diagnostic system, thereby reducing processing complexity while maintaining accuracy.
2Reliability
If real-time data streaming from all robots is implemented, then system monitoring capability is improved, but information retrieval efficiency deteriorates
Solution Approach 1:
The system performs preliminary organization and structuring of data as it is collected, creating standardized snapshots with predefined fields and formats before the data needs to be analyzed. This advance preparation allows for rapid retrieval and filtering when diagnostic queries are executed, reducing information retrieval time while maintaining comprehensive monitoring.
3Measurement precision
If detailed contextual information is captured for each robot behavior, then fault diagnosis precision is improved, but data volume increases
Solution Approach 1:
The system extracts only the most diagnostically relevant contextual information into structured snapshot fields, rather than capturing and storing all possible data. By selectively extracting key parameters, behavior types, timestamps, and robot identifiers, the system maintains high diagnostic precision while reducing overall data volume through targeted information capture.
Data Source
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AI summary
Methods and systems are disclosed to generate and display targeted information related to a plurality of robots working in an operating environment. Plurality of nodes executing at the plurality of robots in communication with plurality of server nodes executing behaviors related to an active plan being executed on the working robots. The nodes running on the robots create Snapshots related to the executing behaviors. Information is then captured based on parent context related to the executing behaviors. The nodes populate a plurality of fields of the Snapshots with values related to at least one or more of captured information, operating environment, and the robots. The Snapshots are closed with a result of the execution of the behaviors. The Snapshots are aggregated and reported by the nodes, as part of the targeted information for display. Customized search queries or visual interfaces can be used to fix or diagnose faults or