| With the rapid development of the new round of technological revolution and industrial transformation,higher requirements have been put forward for the work efficiency and quality of spray painting robots.The emergence of intelligent optimization algorithms has accelerated the realization of this goal.As a product of highly integrated disciplines,robot technology can be better combined with intelligent optimization algorithm technology.Therefore,this paper takes the chicken swarm optimization algorithm and six-axis spray painting robot as the research objects,and improves the chicken swarm algorithm and applies it to the spray painting robot to optimize the trajectory and coating quality of the robot,improve the work efficiency of the robot,and the coating quality of the spray painting.The specific research content and achievements of the paper are as follows:The basic principle of the chicken swarm optimization algorithm(CSO)was studied.Based on the shortcomings of the position update process of the chicken swarm optimization algorithm,an improved chicken swarm optimization algorithm(PDCSO)based on parallel strategy and dynamic constraints was proposed.First,X-best guidance and Levy flight were introduced for the position update of roosters,and the position of these two methods was regulated by parallel strategy.Secondly,a dynamic constraint mechanism for hens to follow the target was proposed for the position update of hens.The effectiveness of the improved method was verified by testing function simulation.The kinematic analysis of the six-axis spray painting robot was carried out,taking the UR5 E six-axis robot arm as an analysis case.The detailed solution process of the forward and inverse kinematics of the UR5 E six-axis robot arm was derived through the standard D-H parameter method.The whole derivation process was verified by simulation in MATLAB with Robotics Toolbox and compared with the UR5 E teach pendant.Finally,the point cloud image of the robot arm workspace was obtained by using the Monte Carlo method.The trajectory planning problem of the robot arm was studied.The differences between trajectory planning in Cartesian space and joint space were analyzed,and the planning processes in both spaces were introduced.The planning processes of straight line trajectory and circular arc trajectory in Cartesian space were described and demonstrated by MATLAB simulation.The differences and shortcomings of third and fifth order polynomial interpolation planning in joint space were analyzed and compared.After analyzing the advantages and disadvantages of other trajectory planning methods,the fifth-order B-spline curve was selected to achieve trajectory planning in joint space,and the construction process of the fifth-order B-spline curve was derived in detail.The trajectory of the robot arm was optimized to improve the speed of the robot arm while reducing the motion impact caused by excessive speed.The optimal motion trajectory was obtained by PDCSO optimization with the optimal objective function constructed based on time-impact.The five-order B-spline curve was used for interpolation,and the torque curves before and after optimization were compared by ADAMS simulation and UR5 E robot arm experiment.It was found that the optimized trajectory moved faster and more smoothly.The advantages and disadvantages of different coating deposition models were compared and analyzed.The distribution model was determined to simulate the distribution of coatings on the sprayed surface.The influence of different moving parameters on dynamic spraying was studied,and the law was verified by experiments.The spraying method of variable parameter alternating spraying was proposed,and a mathematical optimization model was established.The optimal spraying parameters were obtained by PDCSO optimization with the uniformity of coating thickness as the optimization objective.The feasibility and effectiveness of this method were verified by MATLAB simulation and spraying experiment. |
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