Vacuum cleaner
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Solution Overview
Problem
Autonomous-traveling vacuum cleaners face challenges in detecting obstacles due to limitations with ultrasonic and infrared sensors, such as soft curtains or thin cords, which can hinder accurate detection, leading to potential collisions or stagnation.
Innovation Solution
The vacuum cleaner employs cameras positioned on either side of the main casing to capture images, generating a distance image and using a discrimination part to determine if objects are obstacles based on these images, improving detection precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If ultrasonic sensors or infrared sensors are used for obstacle detection, then the vacuum cleaner can detect obstacles to avoid collisions, but soft curtains, thin cords, or black objects cannot be properly detected due to physical limitations of these sensors
Solution Approach 1:
The patent replaces ultrasonic and infrared sensors with a camera-based visual detection system. The camera captures images of obstacles, and image processing algorithms analyze the captured visual information to detect and classify obstacles. This substitution allows the vacuum cleaner to detect objects that are invisible to ultrasonic or infrared sensors, such as soft curtains, thin cords, and black objects, thereby improving both detection reliability and precision.
2Measurement precision
If multiple sensors are used to improve obstacle detection precision, then detection accuracy improves, but the device complexity and cost increase
Solution Approach 1:
The patent employs a camera that serves multiple functions: it captures images for obstacle detection, provides visual information for navigation, and can be used for environmental mapping. By making the camera a multi-functional component, the system achieves high obstacle detection precision without adding multiple specialized sensors, thereby reducing device complexity and cost.
Solution Approach 2:
The patent uses image processing to create a digital representation (copy) of the physical environment. The camera captures visual copies of obstacles, and software algorithms process these copies to identify and classify obstacles. This approach replaces complex physical sensor systems with simpler optical copying and digital processing, reducing hardware complexity while maintaining or improving detection precision.
Data Source
AI summary
A vacuum cleaner having improved obstacle detection precision. The vacuum cleaner includes a main casing, driving wheels, control unit, cameras, an image generation part, and a discrimination part. The driving wheels enable the main casing to travel. The control unit controls drive of the driving wheels to make the main casing autonomously travel. The cameras are disposed apart from each other in the main casing to pick up images on a traveling-direction side of the main casing. The image generation part generates a distance image of an object positioned on the traveling-direction side based on the images picked up by the cameras. The discrimination part discriminates whether or not the picked-up object is an obstacle based on the distance image generated by the image generation part.