Robot Cleaner Selective Image Transmission for Monitoring Efficiency

Resolve Bottlenecks,
Find Innovative Solutions
Generate Solutions

Solution Overview

Problem

Typical robot cleaners transmit continuous surveillance images during monitoring traveling, leading to inconvenience for users due to unnecessary images and failure to alert users to potential dangers when the application is not displayed, and they lack the ability to selectively deliver information based on user needs.

Innovation Solution

A robot cleaner equipped with a deep learning algorithm and machine learning function that captures images at preset intervals, analyzes differences using a controller, and transmits only relevant information to a user terminal, such as presence of people, animals, or obstacles, allowing for optimized surveillance image delivery and improved monitoring performance over time.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of information

If continuous surveillance images are transmitted during monitoring traveling, then the user can receive real-time images of the cleaning area, but the user receives unnecessary images causing inconvenience and cannot be alerted to dangerous situations when the application is not displayed

Engineering Contradiction:
Improveinformation delivery qualityVSAvoiduser convenience
Core Design Contradiction:
Loss of informationVSEase of operation

Solution Approach 1:

The patent extracts and transmits only specific meaningful information (dangerous situations, obstacles, people, animals) from the continuous surveillance images rather than transmitting all images. The controller identifies and extracts key events such as detection of obstacles, people, or animals, and transmits only these extracted information points to the user terminal, filtering out unnecessary continuous image data.

Inventive Principle:
Principle #2Taking out (Extraction)

Solution Approach 2:

The controller acts as an intermediary between the camera system and the user terminal. It processes the continuous image stream, identifies meaningful events, and selectively transmits only relevant information to the user, serving as a mediator that transforms continuous data into discrete meaningful notifications.

Inventive Principle:
Principle #24Intermediary (Mediator)

2Reliability

If all captured images are transmitted to the user terminal, then the user receives complete surveillance data, but the user continuously receives unnecessary images causing inconvenience

Engineering Contradiction:
Improvemonitoring accuracyVSAvoidimage data volume
Core Design Contradiction:
ReliabilityVSQuantity of substance

Solution Approach 1:

The system extracts only meaningful information from the continuous image stream. The controller detects specific events such as obstacles, people, or animals and transmits only these extracted data points rather than all captured images, significantly reducing data volume while maintaining monitoring reliability.

Inventive Principle:
Principle #2Taking out (Extraction)

Solution Approach 2:

Instead of transmitting all captured images (excessive action), the system transmits only the necessary portion of image data that contains meaningful information about dangerous situations or relevant events, achieving partial transmission that optimizes between completeness and efficiency.

Inventive Principle:
Principle #16Partial or excessive action

3Adaptability or versatility

If the robot cleaner uses deep learning technology for image analysis, then the monitoring performance can be improved and adapted over time, but the device complexity increases

Engineering Contradiction:
Improvemonitoring performance adaptationVSAvoiddata processing complexity
Core Design Contradiction:
Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The deep learning model enables the robot cleaner to automatically learn and improve its monitoring capabilities over time. The system performs self-service by continuously learning from new data, adapting to different environments and scenarios, and improving its detection accuracy without requiring manual reconfiguration or external intervention.

Inventive Principle:
Principle #25Self-service

Solution Approach 2:

The deep learning model is pre-trained with extensive training data before deployment. This preliminary training action equips the system with pre-acquired knowledge and capabilities, allowing it to perform complex image analysis and adaptation tasks efficiently during actual operation without requiring real-time computational resources for basic learning.

Inventive Principle:
Principle #10Preliminary action

Data Source

PatentEP3432107B1Cleaning robot and controlling method thereof
Publication Date: 2021.04.07 LG ELECTRONICS INC
  • EP3432107B1 patent drawingFigure 1~3
  • EP3432107B1 patent drawingFigure 4~5
  • EP3432107B1 patent drawingFigure 6

AI summary

In order to solve the task of the present disclosure, a cleaner for performing autonomous traveling according to an embodiment of the present disclosure may include a main body, a driving unit configured to move the main body, a camera configured to capture an image in the vicinity of the main body at preset intervals, and a controller configured to select at least one of a plurality of traveling modes so as to control the driving unit and the camera to perform the selected traveling mode, wherein the controller changes a setting value related to the illuminance of the camera while the camera continuously captures an image.