Self-Learning Floor Processing Robot for Recurring Error Prevention
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Solution Overview
Problem
Automatically moving floor processing devices, such as cleaning robots, often continue to approach locations where previous malfunctions or interruptions have occurred, lacking the ability to prevent recurrence of error situations that prevent further processing.
Innovation Solution
The floor processing device is equipped with a self-learning control unit that analyzes detected parameters using artificial intelligence to predict and prevent error situations by storing and analyzing data on recurring patterns of errors, allowing it to adjust its operations to avoid imminent errors, either automatically or by alerting the user.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the floor processing device continues to approach locations where previous malfunctions occurred, then the device can maintain its cleaning routine and complete the surface processing, but the device repeatedly encounters error situations that prevent it from moving or processing the surface, requiring manual intervention
Solution Approach 1:
The control unit performs preliminary analysis of detected parameters to identify recurring error patterns before the error actually occurs. By detecting parameters and analyzing them for recurring patterns characterized by repeatedly encountered combinations of errors and chronologically preceding environment/device parameters, the system takes preventive action to avoid the error situation entirely, rather than reacting after the error occurs
Solution Approach 2:
The system implements a feedback mechanism where the control unit continuously detects device and environment parameters, analyzes them for recurring patterns, and uses this analysis to predict and prevent imminent errors. The feedback loop allows the device to learn from past errors and adjust its operation to avoid repeating the same error situations
2Reliability
If the floor processing device is equipped with self-learning capability to predict and prevent errors, then the device can avoid error situations and maintain continuous operation, but the device complexity increases due to additional detection and analysis functions
Solution Approach 1:
The control unit performs multiple functions: it controls the driving attachment and floor processing unit, detects device and environment parameters, analyzes detected parameters for recurring patterns, predicts imminent errors, and initiates measures to prevent errors. By consolidating these diverse functions into a single multi-functional control unit, the system achieves error prediction and prevention capabilities without proportionally increasing overall device complexity
Solution Approach 2:
The control unit autonomously detects parameters, analyzes them for recurring patterns, predicts errors, and initiates preventive measures without requiring external intervention. The system serves itself by automatically learning from past errors and adjusting its operation to prevent future errors, reducing the need for manual programming or intervention
Data Source
AI summary
A floor processing device automatically moves within an environment, with a driving attachment, a floor processing unit, an obstacle detection unit, a control unit and a detection unit for detecting device parameters and/or environment parameters control unit is set up to determine an error of the floor processing device based upon the detected parameters that prevents the floor processing device from moving and/or the floor processing device from processing a surface to be processed in such a way that the floor processing device is unable to automatically extricate itself from the error situation. The control unite is set up to analyze the parameters detected by the detection unit with respect to recurring patterns that have a repeatedly encountered combination of an error and at least one chronologically preceding environment and/or device parameter.