Robot Point Cloud Classification for Collision Prediction
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Solution Overview
Problem
Existing collision prediction methods for robots struggle to accurately predict collisions involving robot accessories and in dynamic work environments, leading to decreased processing efficiency and errors in collision determination.
Innovation Solution
A collision prediction method that acquires configuration and posture information of a multi-joint robot, classifies point cloud data into robot and nearby object data, and predicts collisions based on this classification, using a combination of classification and prediction methods to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If point cloud data is classified based on robot configuration and posture information, then collision prediction accuracy is improved, but processing complexity increases
Solution Approach 1:
The point cloud data is segmented into multiple regions based on robot configuration and posture information. The classification unit divides the workspace into regions where collision risks differ, allowing targeted processing of high-risk areas while reducing overall computational complexity.
Solution Approach 2:
The system performs preliminary classification of point cloud data into robot-related and object-related points before collision prediction. By pre-organizing the data based on robot configuration and posture, the system reduces the complexity of subsequent collision detection operations.
2Reliability
If collision prediction includes robot accessories, then prediction comprehensiveness is improved, but processing time increases
Solution Approach 1:
The robot system is segmented into multiple components including main body and accessories. Each component's collision risk is evaluated independently through region classification, allowing comprehensive coverage while optimizing processing time by focusing computational resources on high-risk regions.
Solution Approach 2:
The system performs collision prediction on partially processed point cloud data by classifying only the necessary regions based on robot posture and configuration. This partial processing approach maintains comprehensive prediction coverage while reducing overall processing time.
3Adaptability or versatility
If dynamic work environment changes are accommodated, then system adaptability is improved, but computational load increases
Solution Approach 1:
The classification of point cloud data is made dynamic by using real-time robot posture information and configuration data. The system automatically adapts the classification regions based on current robot state, enabling the system to handle dynamic work environment changes efficiently.
Solution Approach 2:
The system changes classification parameters based on robot configuration and posture. By dynamically adjusting the parameters used for point cloud classification according to real-time robot state, the system adapts to environmental changes while optimizing computational resource usage.
Data Source
AI summary
A collision prediction method for predicting a collision between a multi-joint robot and a nearby object includes a first acquisition step of acquiring configuration information of the robot and posture information of the robot; a second acquisition step of acquiring point cloud data including the robot and the nearby object; a classification step of classifying the point cloud data acquired in the second acquisition step into point cloud data corresponding to the robot and point cloud data corresponding to the nearby object, based on the configuration information of the robot and the posture information of the robot; and a prediction step of predicting a collision between the robot and the nearby object, based on a classification result obtained in the classification step.