Robot Interference Detection Using Decimated Point Cloud Regions
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Solution Overview
Problem
Existing interference check devices for robots are inefficient in processing large point cloud data to determine interference between robot parts and other objects, leading to increased processing loads.
Innovation Solution
An interference determination device that decimates point cloud data based on range and number of points, extracts relevant data within a determination region encompassing the robot model, and determines interference by checking if the extracted data is inside the robot region, thereby reducing the data required for interference assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If point cloud data is used entirely for interference determination, then measurement precision is improved, but processing load increases
Solution Approach 1:
The workspace is divided into multiple determination regions, each corresponding to a specific robot part. Point cloud data is processed and evaluated separately for each determination region rather than processing all point cloud data uniformly. This segmentation allows the system to focus computational resources only on relevant areas, reducing overall processing load while maintaining accurate interference detection for each robot component.
Solution Approach 2:
The system extracts only the point cloud data that falls within the determination regions from the complete point cloud dataset. By filtering and extracting only the necessary data portions related to robot parts and their surrounding spaces, the system eliminates unnecessary data processing while preserving all information needed for accurate interference determination.
2Device complexity
If point cloud data is reduced for processing, then processing load is decreased, but measurement precision may deteriorate
Solution Approach 1:
Different processing approaches are applied to different regions of the workspace. Determination regions that are more critical for interference detection (such as areas around robot joints or end-effectors) receive more detailed processing, while less critical areas use coarser processing. This local quality approach ensures high measurement precision is maintained where it matters most while reducing processing load in less critical areas.
Data Source
AI summary
The interference determination device includes a distance measurement unit measuring a distance to an object contained in a workspace of a robot; a decimation unit decimating point cloud data obtained by the distance measurement unit, based on a range and a number of points of the point cloud data obtained by the distance measurement unit; an extraction unit extracting point cloud data, contained in a determination region encompassing a robot region that corresponds to a model of the robot positioned in the workspace, from point cloud data after being decimated by the decimation unit; and a determination unit determining that the robot interferes with the object when the point cloud data extracted by the extraction unit is located inside the robot region.