Research on 3D Model Data Processing for Multi-Dimensional Surface Forming Motion Planning
The efficiency of reading 3D models and solving spatial target points during the process of multi-dimensional surface forming is crucial.As the complexity of the STL model increases,the number of trian-gular patches increases correspondingly,the computational complexity increases significantly,the solution's efficiency decreases,and the computational difficulty of motion planning and pattern mapping also increa-ses.This paper proposes a 3D model data processing and motion planning method based on deep learning method for the above problems and compares it with the traditional computational planning method.The ex-perimental results show that the method proposed in this paper can effectively improve the positioning speed of model data points by more than 2 times,and the positioning speed increases with the increase of the a-mount of solved point dataand it is robust to the increase of the number of model vertices,which greatly im-prove the speed of motion planning and provide a new idea for solving the motion planning of multi-dimen-sional surface forming.It is suitable for large-scale data calculation and can be applied to production and manufacturing tasks interacting with reality directly in the future.
multi-dimensional surface formingdeep learning3D model data processingmotion planning
NETL
NSTL
万方数据
维普
