Autonomous Trajectory Planning Of Large Complex Component Processing Robot Based On 3D Point Cloud

The grinding of large complex components such as wind turbine blades,aerospace structural components,and ship shells is a complex and arduous task,and is still dominated by manual grinding.In order to improve the quality and efficiency of grinding of large and complex components,it is an inevitable trend for robots to replace manual grinding.In order to realize high-efficiency and high-precision machining of large and complex component robots,it is necessary to study the processes of machining system model,workpiece model processing and surface trajectory planning.The following problems still exist in the above research: the robot processing model relies on manual experimentation and adjustment,which is time-consuming and laborious;the CAD model and the workpiece size are inconsistent after the workpiece is placed or transported,and the machining accuracy of the robot cannot be guaranteed;the large-scale complex component model has large data information.Difficult to handle,resulting in slow and accurate robot trajectory planning.According to the characteristics of large and complex components,this paper proposes a large-scale complex component robot autonomous processing trajectory planning method based on 3D point cloud,and combines OpenCascade geometry engine to develop autonomous processing trajectory planning software for large complex component robots.The main research contents of this paper are as follows:Model the robot end and workpiece in the robot's autonomous machining trajectory planning.Combined with the large size of large complex components and high processing precision requirements,the process parameter model of the robot end and the attitude vector parameter model of the workpiece are established respectively.Based on the Hertzian contact theory,the process parameter model of the robot end is constructed,and the attitude information of the workpiece is solved based on the non-uniform principal component analysis method.Point cloud processing and model reconstruction for large complex components.Based on the iterative near point method,a point cloud space transformation method of target transformation matrix is proposed.The point cloud model is spatially transformed according to the target transformation matrix,and the point cloud information is automatically transformed after the workpiece is moved.The 3D point cloud is sliced by the maximum off-angle constraint.An unordered point sorting algorithm is proposed to simplify the complex point cloud model into an ordered point set.Finally,the three-dimensional reconstruction is completed by B-spline curve fitting.The 3D reconstructed surface is used as the processing object,and the golden section method is used to optimize the key parameters under the processing profile constraint.The trajectory planning is performed using the containment box algorithm and the trajectory point coordinates are obtained.The curve point thinning algorithm is used to reduce the dense track points into trajectory points of equal chord difference,and improve the trajectory planning rate.Combined with the parameter geometry of the surface,the parameter differential value of the track point on the surface is calculated.According to the attitude information of the model,the end vector direction of the robot is automatically constrained,and the robot track information is generated by combining the track point coordinates and the direction vector.Based on the three-dimensional point cloud-based large-scale complex component robot autonomous processing trajectory planning method,a large-scale complex component grinding robot trajectory planning system was designed and developed.Taking the wind turbine blade as the experimental object,the validity of the trajectory planning method and the practicability of the large-scale complex component robot trajectory planning software are verified.