Cleaning prioritization for mobile cleaning robot
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Solution Overview
Problem
Conventional mobile robot scheduling is tedious, prone to human error, and lacks adaptability to user behavior and contextual information, leading to inefficient and inconvenient cleaning operations.
Innovation Solution
A mobile cleaning robot system that generates cleanliness scores for each room based on debris levels, prioritizes cleaning tasks, and schedules operations autonomously using historical data and time-based degradation, allowing for adaptive and user-centric mission planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual scheduling is used for cleaning missions, then the robot can perform cleaning tasks, but the scheduling process is tedious and prone to human error
Solution Approach 1:
The system enables autonomous self-scheduling by allowing the robot to automatically generate cleaning missions based on its own state (battery level, cleaning history) and environmental conditions, eliminating manual scheduling operations entirely
Solution Approach 2:
The system incorporates feedback loops where the robot monitors its own performance, battery consumption patterns, and cleaning effectiveness to continuously optimize and adjust its scheduling decisions, improving both ease of operation and reliability
2Adaptability or versatility
If conventional scheduling methods are used, then cleaning tasks can be assigned, but the system lacks adaptability to user behavior and contextual information
Solution Approach 1:
The scheduling system transitions from static pre-defined routines to dynamic adaptive scheduling that continuously adjusts cleaning priorities and timing based on real-time user behavior patterns, contextual information, and environmental conditions
Solution Approach 2:
The system performs preliminary analysis of user behavior patterns and historical cleaning data to proactively optimize scheduling decisions before executing cleaning missions, enabling better adaptability without compromising productivity
3Device complexity
If uniform cleaning schedules are applied to all rooms, then scheduling is simple, but it does not account for varying debris levels and cleaning priorities
Solution Approach 1:
The system applies local quality by assigning different cleaning priorities, frequencies, and resource allocations to different rooms based on their specific characteristics such as debris generation rates, user activity patterns, and historical cleaning data
Solution Approach 2:
The system dynamically changes scheduling parameters (cleaning frequency, time of day, robot speed, suction power) based on room-specific conditions and priorities, optimizing cleaning effectiveness while maintaining manageable system complexity through modular parameter adjustment
Data Source
AI summary
A method of prioritizing cleaning of rooms of an environment for a mobile cleaning robot can include generating a cleanliness score for each room of the environment using a controller. Each of the cleanliness scores can be indicative of an amount of debris expected in each respective room by the mobile cleaning robot. The method can also include generating a room cleaning priority based at least partially on the cleanliness score of each of the rooms. The method can also include scheduling operations of the mobile cleaning robot for a planned mission based on the room cleaning priority of each of the rooms of the environment.