Robot Cleaner Depth Image Dead Zone Detection for Collision Avoidance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing robot cleaners often collide with obstacles due to inadequate obstacle detection, leading to potential damage to the cleaner or the obstacle, and there is a need for more efficient obstacle recognition.
Innovation Solution
A robot cleaner and method that utilize a depth image to detect obstacles by determining a dead zone in the image, where a dead zone represents an area where distance measurement is not possible due to proximity or strong light, allowing the robot to avoid collisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a robot cleaner travels at a certain speed to perform cleaning efficiently, then productivity is improved, but collision with obstacles occurs leading to damage
Solution Approach 1:
The system performs preliminary obstacle detection by analyzing depth images to identify dead zones before the robot cleaner reaches collision distance. This allows the robot to adjust its path in advance, maintaining high travel speed while avoiding obstacles through proactive navigation adjustments rather than reactive stopping.
Solution Approach 2:
The depth image processing system acts as an intermediary between the robot's movement and physical obstacles. By converting optical information into depth maps and identifying dead zones, it creates a virtual representation of obstacle locations that guides navigation, allowing the robot to maintain speed while avoiding collisions through informed path planning.
2Device complexity
If traditional obstacle sensors are used for obstacle detection, then device complexity is reduced, but measurement precision is insufficient leading to inadequate obstacle recognition
Solution Approach 1:
The system transitions from traditional 2D sensor data to 3D depth image analysis. By processing depth information that provides spatial dimensionality, the system achieves superior obstacle detection precision while maintaining relatively simple hardware. The dead zone identification in depth images enables accurate obstacle recognition without requiring complex multi-sensor arrays.
3Productivity
If the robot cleaner approaches close to walls for efficient cleaning, then cleaning coverage is improved, but misidentification of walls as obstacles causes the robot to stop or collide
Solution Approach 1:
The system applies different interpretation rules to different regions of the depth image. Dead zones indicating walls are distinguished from other obstacles through localized analysis of depth patterns and spatial context. This allows the robot to approach walls closely for cleaning while the system specifically recognizes wall patterns and adjusts navigation accordingly, preventing false obstacle avoidance.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The solution enables the robot cleaner to effectively detect obstacles and avoid collisions, preventing damage to the cleaner and obstacles, while improving the efficiency of obstacle recognition.
Implementation Method 1
a light source for irradiating light, a sensor for sensing that the light irradiated from the light source is reflected
Data Source
AI summary
Disclosed is a robot cleaner including a light source for irradiating light, a sensor for sensing that the light irradiated from the light source is reflected, and a controller that processes an image using the light sensed by the sensor to calculate a distance value of an individual location of the corresponding image, wherein it is determined that there is an obstacle when there is a dead zone in the image processed by the controller.