Robot Selection Area Mapping Using External Sensor Positioning
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Solution Overview
Problem
Current methods for determining selection areas for mobile devices, such as cleaning robots, are cumbersome and prone to errors, especially when identifying inaccessible or dirty areas that need cleaning, as they rely on manual map navigation and sensor limitations.
Innovation Solution
A method using sensor equipment in the environment, such as smartphones or smart home devices, to capture images or sensor data that are linked to the robot's map, allowing for easy selection and specification of areas to be cleaned or avoided, using camera images, Wi-Fi signatures, or other sensors for precise positioning and orientation determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual map navigation is used to determine selection areas, then the mobile device can identify areas to be cleaned or avoided, but the process becomes cumbersome and error-prone
Solution Approach 1:
The patent replaces manual mechanical navigation through maps with automated optical recognition using camera images. The mobile device captures images of the environment, automatically detects selection areas through image processing, and determines their positions without requiring manual map navigation. This substitution of mechanical interaction with automated visual recognition resolves the contradiction by improving accuracy while maintaining ease of operation.
Solution Approach 2:
The mobile device performs self-determination of selection areas by autonomously capturing images, processing them to identify areas to be cleaned or avoided, and calculating positions independently. The device serves itself by eliminating the need for external manual intervention in the area selection process, thereby improving reliability without complicating operation.
2Difficulty of detecting and measuring
If sensor equipment on the mobile device is used, then areas can be detected, but inaccessible or dirty areas remain undetectable due to sensor limitations
Solution Approach 1:
The patent transitions from ground-level sensor detection to aerial or elevated camera imaging to detect areas that are inaccessible to ground-based sensors. By changing the detection dimension to overhead imaging, the system can identify areas behind furniture, under objects, or in other hard-to-reach locations, thereby improving detectability without losing information about hidden areas.
Solution Approach 2:
The patent introduces camera images as an intermediary medium to indirectly detect areas that direct sensors cannot reach. Instead of relying on contact-based or proximity-based sensors that fail for inaccessible areas, the system uses visual imaging as an intermediary to gather information about hidden or unreachable zones, resolving the contradiction between detectability and information completeness.
3Reliability
If user inference based on surrounding walls and obstacles is used, then no-go zones can be identified, but the process becomes time-consuming and susceptible to error
Solution Approach 1:
The patent replaces the manual cognitive process of inferring no-go zones from wall and obstacle patterns with automated image recognition algorithms. The system directly identifies no-go zones from camera images through pattern recognition and image processing, eliminating the time-consuming manual inference process while improving accuracy by removing human error. This resolves the contradiction by achieving both high reliability and time efficiency.
Solution Approach 2:
The patent performs preliminary automated analysis of the environment through image capture and processing before the user needs to define selection areas. By pre-identifying potential no-go zones and selection areas through automated image recognition, the system prepares the information in advance, eliminating the need for time-consuming manual inference and ensuring accurate identification from the outset.
Data Source
AI summary
A method for determining a selection area in an environment for a mobile device, in particular a robot. The method includes: providing sensor data obtained using a sensor system not associated with the mobile device in the environment, the sensor data characterizing a position and/or orientation of an entity in the environment; determining, based on the sensor data, the position and/or orientation of the entity in a map provided for navigation of the mobile device; providing specification data obtained using the sensor data, the specification data characterizing the selection area; determining the selection area in the map based on the specification data; and providing information about the selection area to the mobile device, and in particular instructing the mobile device to correspondingly take the selection area into account when navigating.