Cognitive Vacuum Cleaning for Debris-Aware Route and Power Control
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Solution Overview
Problem
Conventional vacuum cleaners lack efficiency in targeting high-debris areas and optimizing cleaning routines based on environmental patterns and user habits, leading to inefficient energy use and incomplete cleaning.
Innovation Solution
A cognitive cleaner system equipped with a learning module that uses deep neural networks and sensors to analyze debris patterns, user cohorts, and environmental data to modify its cleaning routine, focusing on high-debris areas and adjusting suction power and movement accordingly, while predicting user activities and optimizing battery use.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a conventional vacuum cleaner cleans all areas uniformly, then the cleaning coverage is complete, but the energy consumption increases and time efficiency decreases
Solution Approach 1:
The system implements variable cleaning intensity by adjusting suction power and cleaning parameters based on the specific debris conditions detected in different locations. High-debris areas receive intensified cleaning with higher suction power, while low-debris areas receive reduced cleaning intensity, optimizing energy consumption while maintaining effective cleaning where needed.
Solution Approach 2:
The system performs preliminary scanning and debris detection before the actual cleaning process. By using sensors to identify high-debris areas in advance, the vacuum cleaner can pre-plan its cleaning path and allocate energy resources efficiently, avoiding unnecessary cleaning in low-debris areas and focusing power on areas that require attention.
2Adaptability or versatility
If a conventional vacuum cleaner uses fixed cleaning patterns, then the device complexity is low, but the adaptability to different debris patterns and user habits decreases
Solution Approach 1:
The system continuously scans and detects debris conditions during cleaning operations, using sensor data to provide feedback to the control system. This feedback loop enables the vacuum cleaner to dynamically adjust its cleaning pattern, suction power, and path planning based on real-time debris detection, allowing adaptation to varying cleaning scenarios without requiring complex manual programming.
Solution Approach 2:
The system autonomously learns and adapts to user habits and environmental patterns through embedded algorithms that process cleaning data over time. By automatically analyzing cleaning effectiveness and user preferences, the system self-optimizes its behavior without requiring external intervention or complex user configuration, achieving adaptability through intelligent automation.
3Measurement precision
If a vacuum cleaner scans and analyzes debris data continuously, then the measurement precision of debris locations improves, but the time required for cleaning increases
Solution Approach 1:
The system performs continuous scanning at a lower intensity for general area assessment, then intensifies scanning and data collection only in regions identified as having significant debris. This partial action approach maintains sufficient measurement precision for debris location detection while minimizing the overall time spent on scanning, as the system focuses detailed analysis only where necessary rather than uniformly across all areas.
Data Source
AI summary
A method, system and computer program product for modifying a cleaning routine of a mobile cleaner scans the surface to collect debris data, the debris data including an amount and location of debris on the surface. A profile of the surface is updated with the collected debris data. A profile of the surface is analyzed to identify a debris region on the surface, the debris region including an amount of debris differs from a high threshold. A cleaning routine of the mobile cleaner is modified based on the profile.