Robot Surface Classification Using 3D Depth Boundary Detection
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Solution Overview
Problem
Existing robots face challenges in accurately identifying the characteristics of driving surfaces with high light absorbance or diffused reflection materials, using LIDAR sensors, which hinders optimal service provision.
Innovation Solution
A robot equipped with a 3D depth sensor that acquires depth images of the driving surface, identifies boundary areas with changed tilt information, and determines the type of outside areas based on this information to control its driving accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LIDAR sensor is used to identify driving surface characteristics, then the robot can detect surface information, but it fails to correctly identify surfaces with high light absorbance or diffused reflection materials
Solution Approach 1:
The patent segments the detection task by dividing the driving surface into multiple regions (boundary area with changed tilt information and outside area) and applying different analysis methods to each region. The 3D depth sensor captures depth information that is then segmented into map data with multiple cells, allowing differential processing of surface characteristics.
Solution Approach 2:
The patent transitions from 2D LIDAR scanning to 3D depth sensing by acquiring depth images that capture height and tilt information in three dimensions. This dimensional change enables the robot to detect surface characteristics through depth variations and tilt angles rather than relying solely on light reflection patterns.
2Ease of manufacture
If the robot uses traditional LIDAR-based surface identification, then the system structure remains simple, but the service quality deteriorates on surfaces with high light absorbance or diffused reflection
Solution Approach 1:
The patent replaces the optical-based LIDAR detection system with a 3D depth sensing system that measures physical depth and tilt characteristics. This substitution moves from light-based detection to geometric measurement, enabling reliable surface identification independent of material optical properties.
3Measurement precision
If the robot acquires detailed depth information and performs complex boundary analysis, then surface identification accuracy improves, but processing time and computational load increase
Solution Approach 1:
The patent applies local quality by focusing detailed analysis only on the boundary area where tilt information changes, while treating the outside area differently. This localized approach concentrates computational resources on critical regions rather than uniformly processing the entire field of view.
Solution Approach 2:
The patent performs preliminary actions by first acquiring the complete depth image and generating map data, then identifying boundary areas based on tilt changes before classifying outside areas. This staged approach organizes processing to avoid redundant computations.
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 robot can more accurately identify the characteristics of driving surfaces and determine optimal driving routes, enhancing user convenience and service quality.
Implementation Method 1
a three dimensional (3D) depth sensor... acquire a depth image of a driving surface photographed by the 3D depth sensor
Data Source
AI summary
A robot is provided. The robot includes a driving part, a three dimensional (3D) depth sensor, a memory storing instructions, and a processor connected to the driving part, the 3D depth sensor, and the memory. The processor is configured to execute the instructions to acquire a depth image of a driving surface photographed by the 3D depth sensor while the robot is driving in a space, acquire, based on the acquired depth image, location information of a boundary area where tilt information of the driving surface is changed, acquire type information corresponding to an outside area of the boundary area based on the acquired location information of the boundary area and the changed tilt information, and control the driving part based on the acquired type information.