Robot Vacuum Schedule Generation from Historical Usage Patterns
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing robotic floor-cleaning devices require users to manually set schedules, which can be time-consuming and inefficient, and lack the ability to automatically generate schedules based on historical usage data.
Innovation Solution
A method for a robotic floor-cleaning device to automatically devise a work schedule using historical data, incorporating a control unit, memory unit, and input/output means to monitor usage times, store data, and propose a suggested schedule to users, with the option for user adjustments and continuous database updates, employing machine learning techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If users manually set schedules for robotic floor-cleaning devices, then scheduling functionality is achieved, but user time and effort are consumed
Solution Approach 1:
The system automatically generates cleaning schedules by monitoring and analyzing user manual activation patterns over time. The control unit stores historical data about when users manually turn on the device and autonomously devises optimal schedules based on this data, eliminating the need for users to manually program schedules while preserving personalized cleaning patterns.
Solution Approach 2:
The system performs preliminary data collection and analysis by continuously monitoring manual activation times and storing this information in memory. This preliminary accumulation of usage data enables the subsequent automatic schedule generation to occur seamlessly without requiring user intervention at schedule-setting time.
2Productivity
If simple manual activation is used, then ease of operation is maintained, but no scheduling optimization is achieved
Solution Approach 1:
The control unit autonomously analyzes stored historical activation data and generates optimized schedules without requiring user interaction. The system serves itself by automatically identifying patterns in manual activations and translating these into efficient scheduled operations, thereby improving productivity while maintaining operational simplicity.
Solution Approach 2:
The system continuously monitors actual manual activation times and uses this feedback to refine and update the automatically generated schedules. This closed-loop feedback mechanism ensures that the schedules progressively optimize cleaning productivity while adapting to changing user patterns without increasing operational complexity.
3Productivity
If scheduling systems are added to robotic floor-cleaning devices, then cleaning optimization is achieved, but device complexity increases
Solution Approach 1:
The control unit leverages its existing multifunctional capabilities by repurposing its data processing and control functions to handle schedule generation. Rather than adding dedicated scheduling hardware, the system uses the microprocessor and memory already present for basic control tasks to also perform historical data analysis and schedule optimization, thereby achieving productivity improvement without proportionally increasing device complexity.
Solution Approach 2:
The scheduling subsystem operates autonomously using the device's existing computational resources. The control unit independently processes stored activation data, generates schedules, and updates operations without requiring external scheduling equipment or complex user configuration systems, thus minimizing the increase in overall device complexity.
Data Source
AI summary
Some aspects include a schedule development method for a robotic floor-cleaning device that recognizes patterns in user input to automatically devise a work schedule.