Method and device for controlling floor cleaning robots
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Solution Overview
Problem
Current methods for controlling floor cleaning robots are inefficient as they require manual input of extensive cleaning task information, leading to poor user experience and suboptimal cleaning performance due to the need for users to dissect and assign tasks manually, which can result in uneven cleaning coverage and cleanliness levels.
Innovation Solution
A method and device that obtain overall cleaning task information, determine and transmit specific cleaning control information to multiple floor cleaning robots, allowing them to perform tasks cooperatively by dividing cleaning paths or regions into sub-paths or sub-regions, and assigning these to individual robots based on cleanliness levels and start times, improving efficiency and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual input of cleaning task information is required, then users can control cleaning robots, but user burden increases and operation becomes complex
Solution Approach 1:
The cleaning robot system automatically performs task decomposition and assignment without requiring manual user input. The system self-services by autonomously analyzing cleaning requirements, dividing cleaning paths into sub-paths, and allocating tasks to appropriate robots based on their states, thereby eliminating the need for complex manual operation while reducing user burden.
Solution Approach 2:
The system automatically segments the overall cleaning task into multiple sub-tasks and sub-paths. By dividing the cleaning path into manageable segments and automatically assigning them to different robots, the system reduces operational complexity while maintaining effective control over multiple cleaning units.
2Productivity
If multiple cleaning robots are deployed, then cleaning efficiency improves, but task assignment and coordination becomes complex
Solution Approach 1:
The task assignment system dynamically adjusts robot assignments based on real-time robot states, cleaning progress, and environmental conditions. This dynamic allocation optimizes cleaning efficiency by automatically matching tasks to the most suitable robots while adapting to changing conditions, thereby managing multi-robot coordination without requiring complex static planning.
Solution Approach 2:
The system changes assignment parameters automatically based on robot states, cleaning requirements, and performance metrics. By dynamically adjusting assignment criteria such as robot location, battery level, and current task status, the system achieves high cleaning efficiency with multiple robots while keeping the assignment mechanism relatively simple and adaptive.
3Productivity
If cleaning path is divided into sub-paths manually, then task allocation can be done, but user workload increases and time is consumed
Solution Approach 1:
The system performs preliminary automatic path segmentation and task decomposition before actual cleaning operations begin. By pre-processing the cleaning path into sub-paths and preparing task assignments in advance based on robot availability, the system enables rapid task allocation without requiring users to spend time on manual path division, thereby improving productivity while minimizing user time investment.
Solution Approach 2:
The system replaces manual mechanical path division with automated computational algorithms. Instead of users physically or manually dividing cleaning paths, the system uses automated path planning algorithms to segment cleaning areas and allocate sub-paths to robots, dramatically reducing user time while maintaining or improving task allocation efficiency.
4Manufacturing precision
If users dissect and assign tasks manually, then control over cleaning robots is achieved, but cleaning coverage becomes uneven and cleanliness levels vary
Solution Approach 1:
The system implements feedback mechanisms that continuously monitor cleaning progress, robot positions, and cleaning quality. Based on this feedback, the system dynamically adjusts task assignments and path allocations to ensure uniform cleaning coverage. This automated feedback-driven adjustment achieves consistent cleanliness levels across all areas without requiring users to manually balance task distribution, thereby maintaining ease of operation while improving coverage uniformity.
Data Source
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AI summary
The present disclosure relates to a method and a device for controlling floor cleaning robots. The method includes obtaining (S101) cleaning task information that includes an overall cleaning task; determining (S102) cleaning control information for a plurality of floor cleaning robots based on the cleaning task information, wherein the cleaning control information includes control information for each one of the plurality of floor cleaning robots; and transmitting (S103) the cleaning control information to each one of the plurality of floor cleaning robots, so that each one of the floor cleaning robots performs a cleaning task based on the corresponding cleaning control information.