Path Optimization of Welding Robot Based on Ant Colony and Genetic Algorithm

Abstract

While the process of intelligent industrial production is accelerating, the application scope of welding robots is also expanding. For the purpose of reducing the work efficiency and time consumption of the welding robot, the ACO is used for the shortest distance and the GA is used for the shortest time fixed-point path trajectory optimization. The application of parameter optimization and random disturbance factor in the ACO increases the global search performance of the algorithm. In the shortest time trajectory optimization, the B-spline curve interpolation method and the GA are combined to carry out the segmental optimization processing. Simulation experiments show that the optimization strategy of ACO can increase the iterative calculation efficiency and path optimization performance of the algorithm. At the same time, the robot with optimized genetic algorithm has smaller fluctuations in joint angle and angular velocity in the simulated welding task, and the optimization algorithm takes 17.6 s less than the traditional particle swarm algorithm and 11 s less than the single $A^{\ast }$ algorithm. The experiments confirmed the performance of the ACO-GA for the path optimization of the welding robot, and research can provide a scientific path optimization reference for the welding task of the industrial production line.