Robot Capability State Tree for Real-Time Error Introspection
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Solution Overview
Problem
Existing technologies for robot error identification and reporting, such as Diagnostic System for Robots Running ROS and RoboEarth, focus on static capabilities and do not efficiently categorize, report, or introspect dynamic anomalies in real-time.
Innovation Solution
A method and system using an apparatus state data structure with capability, component state, diagnostic, and event storage data fields, encoded in a hierarchical tree structure, to dynamically represent and manage robot capabilities, errors, and critical events, enabling efficient error categorization, reporting, and introspection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional diagnostic systems (ROS, RoboEarth) are used for error identification and reporting, then error detection capability is provided, but real-time dynamic anomaly categorization and introspection efficiency deteriorates
Solution Approach 1:
The system segments the robot state into distinct capability data fields, each representing specific functionalities (navigation, manipulation, perception). This segmentation allows efficient monitoring and categorization of anomalies by capability type, enabling real-time processing without overwhelming complexity.
Solution Approach 2:
The patent introduces a hierarchical tree structure that adds dimensional organization to error reporting. Errors are categorized not only by type but also by their impact on specific capability data fields and associated data fields, creating a multi-dimensional classification system that improves both detection and introspection efficiency.
2Measurement precision
If comprehensive state monitoring is implemented for all robot capabilities, then anomaly detection accuracy is improved, but system complexity increases
Solution Approach 1:
By dividing the robot state into discrete capability data fields with associated data fields, the system achieves comprehensive monitoring without monolithic complexity. Each capability can be independently monitored and analyzed, reducing the cognitive and computational burden of managing complete state information.
Solution Approach 2:
The hierarchical tree structure serves multiple functions simultaneously: it organizes capability information, tracks associated data fields, categorizes errors, and enables introspection. This universal structure reduces overall system complexity by consolidating multiple monitoring functions into a unified framework.
3Ease of manufacture
If static capability representation is used (as in RoboEarth), then implementation simplicity is maintained, but dynamic anomaly tracking capability deteriorates
Solution Approach 1:
The system transitions from static capability definitions to dynamic capability data fields that are calculated at runtime based on current robot state. This allows the robot to adapt its capability representation in real-time, tracking dynamic anomalies while maintaining a structured approach through the hierarchical tree organization.
Solution Approach 2:
The patent changes the parameter representation from fixed static values to dynamic state-dependent values. Capability data fields and their associated data fields are updated based on current robot state, enabling dynamic anomaly tracking while the hierarchical structure maintains implementation simplicity through consistent organizational patterns.
Data Source
AI summary
An apparatus state data structure for controlling a technical apparatus includes at least one capability data field and at least one associated data field. Each capability data field indicates a respective functionality of the technical apparatus. Each associated data field is associated with a respective capability data field. The at least one associated data field includes at least one required component state data field and at least one required diagnostic data field. Each required component state data field indicates a configuration of a respective component required for the functionality of the capability data field associated with the respective required component state data field. Each required diagnostic data field indicates a respective operational state of a component of the technical apparatus required for the functionality of the capability data field associated with the respective required diagnostic data field.