Research On Motion Planning Algorithm Of Laser Decontamination System

A large number of waste workpieces with radioactive contamination will be produced after the decommissioning of nuclear facilities,laser ablation decontamination is needed to remove the radionuclide deposited on the surface of the waste workpieces.Due to the presence of radioactive contamination,the entire cleaning process must be done without manual operation.Therefore,a robot is used as actuating equipment to carry a laser ablator to clean the waste workpiece.The robot motion planning algorithm will play an important role in this process.Motion planning directly determines the operation efficiency and motion performance of robots and is an important research topic in the field of robot automatic control.Based on the laser decontamination system,this paper developed an algorithm for robot motion planning using the point cloud data of the waste workpiece.Firstly,the point cloud data is processed to extract the laser ablation spot.After all the ablation points are extracted,the global decontamination path planning and the robot local trajectory planning between each two ablation points are carried out.The main research contents are as follows:(1)The principle and overall procedure of the laser decontamination system are analyzed,and the workflow of the system is planned.Which is,the robot carries the laser scanner to scan the workpiece to get the point cloud data of the workpiece surface.After the motion planning is completed,the robot carries the laser ablator to complete the ablation and decontamination according to the results of the motion planning.Then the influence of measuring module,motion module,fixture,and laser ablator on motion planning and the key parameters of each part of the system is analyzed.(2)The point cloud data processing is studied,and the ablation point location extraction algorithm is proposed.After the point cloud denoising is completed,the point cloud structured process is carried out,which reduces the complexity of the following process.Then,by analogy with the clustering algorithm,a point cloud global optimization segmentation algorithm is proposed,which can segment the point cloud data differently according to different parameters.Each subregion corresponds to a point location to be processed.Finally,interference detection is performed to determine the pose of the robot end-effector.(3)The path planning algorithm of the robot is studied.Based on the combination of path and trajectory information,a mathematical model of path planning for response trajectory information is proposed and solved by an ant colony algorithm.In order to improve the shortcomings of the ant colony algorithm,which is easy to fall into local optimum,two kinds of improvement measures are proposed.Numerical simulation shows the effectiveness of the proposed algorithm.(4)The trajectory planning method of the robot is studied,and the trajectory planning goal is selected as the minimum jerk planning.Bernstein polynomial is used to fit the trajectory curve.Then an improved chaotic particle swarm optimization algorithm is proposed to solve the problem.The effectiveness of the method is verified by numerical simulation and actual operation tests of the system.