Ladle Collision Detection Algorithm Based on Three-dimensional Point Cloud Data

To solve the problem of collision detection between the ladle and the object in the workshop during crane movement, a fast collision detection algorithm is proposed based on large-scale 3D point cloud data. The implementation of the algorithm is divided into three stages: model building, point cloud data reading, and collision detection. Firstly, the physical model of the ladle is reasonably abstracted into collision detection points in five directions, and the detection range is set according to the actual application situation, simplifying the collision detection problem without affecting the algorithm's accuracy. Secondly, according to the distribution characteristics of data points in the point cloud file, the large-scale point cloud data file is preprocessed to generate an index table, which is used to optimize data reading and reduce unnecessary I/O collision detection operations. Finally, to meet the algorithm's real-time requirements in the production process, the algorithm is optimized using multi-threaded parallel processing technology. Experiments show that the multi-threaded parallel algorithm works much better in real time than the single-threaded algorithm and can improve the efficiency of collision detection by a large amount.