Cleaning Robot AI-Driven Guard Control for Object Manipulation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional cleaning robots are limited by their sensor combinations, which restrict their ability to obtain specific information about objects in their vicinity, leading to repetitive pattern-based obstacle avoidance without adapting to different objects, necessitating a method to determine optimal tasks based on identified objects.
Innovation Solution
A cleaning robot equipped with a camera, processor, and a guard portion that captures images of objects and applies them to trained AI models to determine tasks, controlling the guard portion to descend and move objects, allowing for tailored actions such as avoidance or relocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a general cleaning robot uses only basic sensors for obstacle detection, then the device complexity is low, but the measurement precision of object information is insufficient, leading to repetitive pattern-based avoidance
Solution Approach 1:
The cleaning robot segments object recognition into multiple stages: initial obstacle detection by basic sensors, detailed image capture by camera, and AI-based classification. This segmentation allows the system to achieve high measurement precision for object information while keeping the overall device complexity manageable by only activating advanced sensors when needed.
Solution Approach 2:
The camera acts as an intermediary between the basic sensors and the AI processing system. When basic sensors detect an obstacle, the camera captures detailed images that serve as intermediate data, enabling the AI model to precisely classify the object type without requiring all advanced sensors to operate continuously, thus balancing measurement precision and device complexity.
2Productivity
If the cleaning robot performs only avoidance driving based on limited sensor data, then the ease of operation is maintained, but the productivity is reduced due to inability to clean obstructed areas
Solution Approach 1:
The cleaning robot uses AI-based object classification to automatically determine the appropriate task without human intervention. The system self-services by classifying objects (e.g., distinguishing between movable items like shoes and immovable structures like vents) and autonomously selecting whether to avoid, push, or clean around them, thereby increasing productivity while maintaining ease of operation.
Solution Approach 2:
The robot changes its operational parameters based on AI-classified object attributes. When an object is identified as movable (low height, lightweight), the robot changes from avoidance mode to pushing mode. This dynamic parameter adjustment based on object characteristics enables the robot to clean previously obstructed areas, significantly improving productivity without requiring complex manual task programming.
3Adaptability or versatility
If the cleaning robot uses AI model to determine optimal tasks based on object identification, then the adaptability to different objects is improved, but the loss of time for image processing and analysis increases
Solution Approach 1:
The system performs preliminary action by pre-training AI models with extensive object data before deployment. During operation, the pre-trained model rapidly classifies objects without requiring time-consuming analysis. The robot also performs preliminary object detection with basic sensors before activating the camera, so AI processing only begins when necessary, reducing overall processing time while maintaining high adaptability.
Solution Approach 2:
The cleaning robot implements periodic action by capturing images at specific intervals or when triggered by basic sensor detection rather than continuously. The AI model processes images periodically based on these triggers, enabling the robot to adapt to different objects with high accuracy while minimizing the time spent on image processing during navigation and cleaning operations.
4Productivity
If the cleaning robot descends the guard portion to push objects, then the productivity is improved by clearing obstacles, but the force required increases, potentially damaging the robot or objects
Solution Approach 1:
The guard portion is designed with local quality variations: it has a large surface area for distributed force application to prevent object damage, while the pushing action is localized to the front edge for effective obstacle clearance. The AI system also applies local quality by selecting push actions only for suitable objects (e.g., lightweight movable items) rather than all obstacles, improving productivity while controlling the force applied to each specific object type.
Data Source
Figure 1A~1B
Figure 2~3A
Figure 3B~4A
AI summary
A method, performed by a cleaning robot, of performing a task, is provided. The method includes capturing an image of an object in a vicinity of the cleaning robot, determining a task to be performed by the cleaning robot, by applying the captured image to at least one trained artificial intelligence (AI) model, controlling a guard portion to descend from in front of an opened portion to a floor surface of the cleaning robot, according to the determined task, and driving towards the object so the object is moved by the descended guard portion.