3D Sensor Cavity Detection in Mining Robots
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Solution Overview
Problem
Existing robotic systems for detecting cavities in mining walls are inefficient and inaccurate due to the dark and irregular environments, leading to slow and unreliable detection processes, especially when using 2D cameras, which require constant illumination and are prone to noise and scanning drift.
Innovation Solution
The use of 3D sensors like RGB-D cameras to obtain point cloud data, which is then analyzed in lower dimensional canonical spaces to identify cavities through topological constrained manifold analysis, boundary detection, and multi-cavity detection using algorithms like Hough-transform and RANSAC, allowing for unsupervised detection and robust positioning of robot tools for explosive charging applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If 2D cameras are used for cavity detection, then the system can operate with simpler hardware, but detection accuracy and reliability deteriorate due to noise and scanning drift in dark environments
Solution Approach 1:
The patent transitions from 2D camera imaging to 3D point cloud data acquisition using RGB-D sensors. This dimensional change enables direct measurement of depth and spatial coordinates, eliminating the noise and scanning drift issues inherent in 2D image processing, particularly in dark mining environments where 2D cameras fail.
Solution Approach 2:
The patent introduces 3D point cloud data as an intermediary representation between the physical cavity structure and the detection algorithm. This intermediary format preserves geometric information while enabling robust processing through manifold analysis and boundary detection, overcoming the limitations of direct 2D image analysis.
2Ease of manufacture
If 2D cameras are used for cavity detection, then the system is easier to implement, but detection speed and reliability worsen due to constant illumination requirements and environmental constraints
Solution Approach 1:
By switching to 3D point cloud data from RGB-D sensors, the system achieves faster detection speeds without sacrificing ease of implementation. The 3D data format enables direct spatial reasoning and accelerates cavity identification through manifold analysis, while the sensors can operate in dark environments without requiring constant illumination.
3Device complexity
If traditional cavity detection methods are used, then the detection process is simpler, but reliability deteriorates in dark and irregular mining environments
Solution Approach 1:
The patent employs 3D point cloud data as an intermediary that captures the irregular geometry of mining walls and cavities accurately. This intermediary format enables reliable detection in dark and irregular environments by preserving full spatial information, which is then processed through manifold analysis to identify cavities with high reliability.
Solution Approach 2:
The patent replaces traditional optical imaging mechanisms with 3D sensing and computational manifold analysis. This substitution eliminates the illumination requirements of optical cameras and provides robust detection in irregular environments through mathematical analysis of point cloud data structures.
4Productivity
If multiple cavities are detected simultaneously, then productivity increases, but computational complexity and processing time increase
Solution Approach 1:
The patent segments the cavity detection process into distinct stages: point cloud acquisition, manifold analysis, boundary detection, and cavity identification. This segmentation enables efficient processing of multiple cavities simultaneously by dividing the computational task into manageable steps, each optimized for specific operations on the point cloud data.
Solution Approach 2:
By operating in 3D point cloud space rather than 2D image space, the patent achieves efficient multi-cavity detection. The 3D manifold analysis provides a compact representation that captures multiple cavities simultaneously, reducing computational complexity compared to processing multiple separate 2D images while maintaining high productivity.
Data Source
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AI summary
A method for operating a robot includes detecting a plurality of cavities in a structure. The detection of the plurality of cavities in the structure include operating a 3D sensor to obtain 3D point cloud data of the structure; analyzing the 3D point cloud data at a lower dimensionality to eliminate irregularities; performing boundary detection of the 3D point cloud data at the lower dimensionality; and performing a multi-cavity detection of the 3D point cloud data to detect the plurality of cavities. The robot may be directed to a cavity of the plurality of cavities. A robotic operation may be performed on the cavity using the robot.