Cleaning robot traveling using region-based human activity data and method of driving cleaning robot
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Solution Overview
Problem
Conventional cleaning robots lack the ability to prioritize cleaning regions based on human activity data, leading to inefficient cleaning as they follow predetermined paths without considering the necessity of cleaning in different areas.
Innovation Solution
A cleaning robot that collects human activity data using user terminals or image-information-collecting devices to determine low-activity regions, where dust or foreign substances tend to accumulate, and autonomously adjusts its path to preferentially clean these areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If the cleaning robot follows a predetermined path for cleaning, then the cleaning robot can operate autonomously without human intervention, but the cleaning efficiency is low because the robot cannot prioritize regions that need cleaning most
Solution Approach 1:
The cleaning robot receives human activity information from user terminals or image-information-collecting devices, processes this feedback data to identify low-activity regions, and adjusts its cleaning path accordingly. This feedback mechanism enables the robot to prioritize regions where dust accumulates most, resolving the contradiction between autonomous operation and cleaning efficiency.
2Quantity of substance
If the cleaning robot cleans all regions uniformly, then the entire movement space is covered, but time is wasted on regions where dust does not accumulate significantly
Solution Approach 1:
The cleaning robot applies different cleaning strategies to different regions based on local characteristics. By analyzing human activity data, the robot identifies regions with low human activity where dust accumulates most, and prioritizes these areas for cleaning. This local quality approach ensures comprehensive coverage while minimizing time spent on regions requiring less cleaning attention.
3Productivity
If the cleaning robot uses human activity data to prioritize cleaning regions, then cleaning efficiency is improved by targeting high-dust areas, but the device complexity increases due to additional data collection and processing requirements
Solution Approach 1:
The cleaning robot uses user terminals or image-information-collecting devices as intermediaries to collect human activity data. These intermediary devices handle the complex tasks of data collection, processing, and transmission, allowing the cleaning robot to benefit from intelligent path planning without bearing the full burden of system complexity. This intermediary approach resolves the contradiction between improved cleaning efficiency and increased device complexity.
Data Source
AI summary
The present disclosure provides a cleaning robot that communicates with peripheral devices over a 5G communication network and that preferentially cleans a region that needs to be cleaned the most based on the communication. Based on information about the use of a user terminal used in a movement space in which the cleaning robot moves or a collecting terminal collecting information about the behavior or locations of a user moving in the movement space, the cleaning robot preferentially moves to a region in which the user terminal or the collecting terminal is rarely used. That is, a region in which a user rarely travels, i.e. a region in which a large amount of dust or foreign substances is liable to accumulate, is cleaned preferentially by the cleaning robot, whereby the cleaning efficiency of the cleaning robot is improved.