Reserch On Automatic Planning And Optimization Of Robotic Painting Trajectories On Complex Surfaces

With the continuous development of global industry,spray paint robots have shown their irreplaceable position in the automotive,furniture and other manufacturing industries.However,the traditional spray gun model,model technology for workpieces to be sprayed,and the spray path optimization algorithm can no longer meet the current demand for spray painting technology.Therefore,in order to complete high-efficiency and low-cost painting tasks and adapt to complex spraying targets,the simple and high-precision modeling of spraying equipment and workpieces and the update of existing spraying optimization algorithms have become important topics in the development of the domestic painting industry.In this paper,the problems of optimization of trajectory optimization for painting robots in complex free-form surfaces are studied,including the establishment of film growth rate model and its application,free-form surface segmentation and path planning,free combination of spray trajectory,and optimization of trajectory of the joints of spray robot arms.In the establishment and application of the film growth rate model,the model of spray gun's position and posture was firstly established by two three-dimensional vector functions.And then based on experimental data and operating experience,and the reasonable assumption can be made on the spray equipment.In this way,the growth rate model of the paint film on the plane and on the arc surface at uniform speed was established.It was proposed that the uniformity of the paint film at the boundary of the complex free-form surface was solved by adjusting the spray rate,and the film growth rate model is simplified by using the least-square fitting method to find the best spray spacing to ensure the film uniformity of two parallel spray trajectories.Finally,the effectiveness of the film growth rate model,the practicability of the least-squares fitting model,and the feasibility of the proposed two optimization methods are verified with the actual spray pattern and simulation results.In free-form surface slicing and path planning,the complex surface was first projected,and the slicing rules and path planning rules on a single chip were proposed.Then,the piecewise evaluation function and the path planning evaluation function were established based on the fragmentation rules and path planning rules respectively.Finally,the four surface slicing schemes of the same free-form surface was evaluated by the piecewise evaluation function.Two sets of surface slicing schemes with the best performance and the worst performance were singled out and routed separately,andthen the path evaluation function was used to evaluate the two-slice path.In the free combination of spray trajectory,modeling was firstly performed to transform it into a traveling salesman problem,and an ant colony-bee colony hierarchical method based on swarm intelligence algorithm was proposed.The group intelligent algorithm for solving problems in this method was divided into two parts: a method based on path construction and a method based on path improvement.Firstly,the ant colony algorithm was used to construct the path,which provided a better initial solution for the bee colony algorithm,and then the bee colony algorithm is used to improve the path to achieve the optimal or near-optimal solution.With this method,it was possible to obtain a faster solution than the ant colony algorithm and also obtain a solution with better performance than the bee colony algorithm.In the simulation experiment,the ant colony-bee colony hierarchical method,the traditional ant colony algorithm and the traditional bee colony algorithm were used to calculate the ten sets of traveling salesman problem,which verified the effectiveness of the proposed method.In the trajectory optimization of the joint of robot arm,it was firstly modeled and then two target functions based on the third derivative of the joint position were established,and a particle swarm-K mean value algorithm were proposed to quickly solve the problem.This method added a K-mean value clustering method based on the particle swarm optimization method to quickly search for the optimal solution,which made the algorithm greatly reduce the computer's running time on the basis of ensuring good performance of the optimal solution.Finally,for the trajectory optimization of the joint of spray paint robot,the particle swarm-K mean value algorithm was used to solve the same problem under the same initial conditions.Compared with the traditional GA algorithm,the fastness and accuracy of the algorithm were verified.