Robot Vacuum Optical Sensing for Early Cliff and Wall Detection
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Solution Overview
Problem
Conventional auto clean machines, such as sweep robots, rely on light sources below the machine to detect cliffs, which can lead to the machine protruding and potentially falling if it cannot determine the cliff's presence in time, as the brightness value of reflected light does not decrease until it is too close, and they struggle to detect walls or object heights effectively.
Innovation Solution
An auto clean machine equipped with a light source to illuminate regions outside and in front, featuring two image sensing areas with different resolutions to capture brightness distributions, allowing the processor to generate detection results for walls, cliffs, and object heights, enabling the machine to control its movement and avoid collisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a light source below the machine is used to detect cliffs, then the machine can detect cliffs when close, but the machine cannot detect cliffs from a distance and may protrude and fall
Solution Approach 1:
The patent transitions from a single-point brightness detection method to a two-dimensional image sensing approach. By capturing brightness distribution across multiple pixels arranged in rows and columns, the system can detect cliffs from a distance by analyzing the spatial pattern of reflected light, not just the overall brightness level.
Solution Approach 2:
The image sensing area is divided into multiple pixel units arranged in specific rows and columns. This segmentation allows the system to analyze different regions of the light region independently, enabling distance-based cliff detection by comparing brightness values across multiple segments rather than treating the entire field as a single measurement point.
2Reliability
If the machine waits until the cliff is below it to detect, then detection is reliable, but the machine cannot stop in time and may drop down
Solution Approach 1:
The system performs preliminary cliff detection by analyzing the brightness distribution pattern in the light region before the cliff reaches the machine's position. By identifying the characteristic pattern of reflected light from distant cliffs, the system can trigger preventive stopping actions in advance, ensuring the machine halts before reaching the cliff edge rather than waiting for immediate proximity detection.
3Device complexity
If a single image sensing area is used, then the device is simple, but the machine cannot detect both walls and cliffs effectively
Solution Approach 1:
The patent assigns different functional roles to different regions of the image sensing area. Specific rows or columns of pixels are optimized for detecting vertical features (walls) while other regions are optimized for detecting horizontal features (cliffs). This local differentiation allows a single sensing area to perform multiple detection functions by analyzing brightness distributions in different spatial zones.
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
Enables the detection of cliffs from a safe distance, allowing the machine to avoid falling and also detect walls or object heights, thereby preventing collisions and ensuring safe operation.
Implementation Method 1
an image sensor 103 to detect reflected light reflected from the ground
Data Source
AI summary
An auto clean machine, comprising: a light source configured to emit light to illuminate at least one light region outside and in front of the auto clean machine; a first image sensing area, configured to sense a first brightness distribution of the light region; a second image sensing area below the first image sensing area, configured to sense a second brightness distribution of the light region; and a processor, configured to control movement of the auto clean machine according the first brightness distribution and the second brightness distribution. The processor generates a wall detection result based on the first brightness distribution of the light region, generates a cliff detection result based on the second brightness distribution of the light region, and controls the movement of the auto clean machine according to the wall detection result and the cliff detection result.