Robot Automatic Drilling And Riveting Path Planning

With the development of automation and intelligent manufacturing,China’s aviation manufacturing industry is facing unprecedented opportunities and challenges.Drilling and riveting is a key link in the manufacturing process of aircraft.The quality of riveting directly determines the working effect of the subsequent processes,and also relates to the safety of aircraft.At present,the original manual drilling and riveting method is still used for civil large passenger aircraft in China,which has low efficiency,great harm to human body,environmental pollution and difficult accuracy guarantee.Therefore,vigorously developing robot automatic drilling and riveting technology has become an important direction of domestic intelligent manufacturing.Plenty of aircraft parts facing holes.Under the premise of avoiding collision,the drilling and riveting work of all holes can be completed in the shortest time,that is,drilling and riveting path planning,which is of great significance to improve the working efficiency of drilling and riveting robot.In view of the current situation and shortage of domestic robot automatic drilling and riveting path planning,the following work is done in this paper:Firstly,according to the drilling and riveting process of aircraft panel,the drilling and riveting objects,riveting process and manufacturing resources are analyzed.In solving irregular problems,the advantages of various intelligent algorithms are compared and the appropriate algorithm is selected.Secondly,genetic algorithm and ant colony algorithm are used to solve the traveling salesman problem.Firstly,the ant colony algorithm is improved by using 2-OPT strategy to improve the defect that ant colony algorithm is easy to fall into the local optimal solution.At the same time,the direction factor is introduced to provide the direction of ant selection.The improvement of genetic algorithm is through Pareto optimization,which changes the excellent individuals directly from the parent generation to the offspring,so as to avoid the destruction of the excellent individuals.After that,the adaptive crossover mutation strategy is adopted to improve the iteration efficiency of the algorithm in the later period.Thirdly,the genetic algorithm is used to initialize the ACO,and then the ACO is switched to the ACO.The positive feedback property of the ACO is used to continue the search and get the final result.Through the simulation of the drilling and riveting path of a single robot,and the drilling and riveting system is extended to the cooperative operation of two robots,the effectiveness of the improved algorithm is verified by comparing with the results of the unimproved algorithm.Finally,a three-dimensional model of the aircraft wing and the end-effector was established,and a dual robot drilling and riveting workstation was built.The tasks of the robot and the end-effector were defined,and the drilling and riveting point location distribution,collision processing and collaborative interference analysis were completed.Finally,the path is generated and the off-line program of the hole making task is output.