Robot State Transition Control for Failure Prevention
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Solution Overview
Problem
Existing techniques for managing robot operations in environments with humans, such as factories, require manual setting of allowable ranges for state and event transitions, and do not allow for automatic updates of sensor output data patterns or recovery from operation failures.
Innovation Solution
A control device with a state transition determination section that automatically controls a robot to perform a predetermined process before a task fails, by identifying and following a preset failure state transition, and includes mechanisms for updating failure state transitions and performing recovery actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual setting of allowable ranges for state and event transitions is used, then the robot operation can be monitored, but the system complexity and operation difficulty increase
Solution Approach 1:
The robot performs self-diagnosis by automatically determining its own state transitions without requiring manual configuration of allowable ranges. The state determination section autonomously identifies current states and transitions based on sensor data, eliminating the need for complex manual setup while maintaining monitoring reliability
Solution Approach 2:
The system changes from fixed manually-set allowable ranges to dynamic state determination based on sensor data patterns. By transforming the monitoring approach from static parameter comparison to dynamic state recognition, the system reduces complexity while improving reliability
2Reliability
If manual configuration of time-series sensor output patterns is required, then operation monitoring is possible, but automation and productivity decrease
Solution Approach 1:
The robot autonomously determines its own state transitions by analyzing sensor data patterns without requiring manual configuration. The state determination section automatically identifies current states and transitions, enabling the system to self-monitor and self-diagnose operational status
Solution Approach 2:
The system continuously monitors sensor data and provides feedback to the state determination section, which automatically adjusts state recognition based on observed patterns. This closed-loop feedback mechanism enables automatic adaptation to different operational scenarios without manual reconfiguration
3Adaptability or versatility
If the robot operates based on preset rules or machine learning models, then operation flexibility is achieved, but the ability to determine operation failure is lost
Solution Approach 1:
The system pre-defines state transitions that correspond to failure conditions before operation begins. By establishing failure state transitions in advance and comparing real-time state determination against these predefined failure patterns, the robot can detect failures while maintaining operational flexibility
Solution Approach 2:
The state determination section acts as an intermediary between the flexible machine learning-based operation control and the failure detection requirement. It translates sensor data into discrete states and compares these against failure state transitions, enabling failure detection without constraining the underlying operational flexibility
Data Source
AI summary
There is provided a control device, a control method, and a program that can prevent failure of an operation of a robot. The control device according to an aspect of the present technology includes a state transition determination section that controls an operation of a robot to perform a predetermined process before a task results in failure in a case where a state transition of the robot in performing the task follows a failure state transition that is preset as a state transition that results in the failure of the task. The present technology can be applied to a robot capable of autonomous operation.