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 ∗ 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.

Review of path planning of welding robot based on spline curve

“…The traditional path optimization method of weld sequence selection based on experience has large time consumption and limited accuracy. In this example, the Ant Colony Algorithm (ACO) is used for parameter optimization, which improves the global search performance of the algorithm [127]. Then the B-spline curve interpolation method is combined with genetic algorithm to carry out segmentation optimization, which greatly reduces the time of path planning.…”

“…A penalty function is introduced into the trajectory planning of the total B-spline curve equation to determine the fitness function of the genetic algorithm, as shown in Eq. ( 13) [127].…”

“…Figure 6. Comparison of robot performance between single B spline curve method and optimized joint algorithm [127] In the realm of industrial robot path planning, the combined utilization of intelligent algorithms and spline curves not only facilitates the determination of the most efficient welding path but also empowers the robot to navigate through intricate and challenging working environments and constraints with agility. This integrated approach serves to significantly improve overall work efficiency in welding operations.…”

Suitable for loading and distributing modular containers

“…First, suppose that the distribution center has n tickets of goods with the same destination to be delivered, and their weights are g1 and g2 respectively,... gn, the volumes are v1 and v2,... vn, the vehicle load used to load the cargo is G, the effective volume is V, i is the cargo number, i = 1, 2,... n, gi is the weight of the cargo, vi is the volume of the cargo, under the premise of not overloading, it is required which goods the vehicle should load to make the capacity utilization of the vehicle the highest [3] . When xi=1 loads the goods into the carriage [4] .…”

Multi-Objective Immune Optimization of Path Planning for Ship Welding Robot

“…Gao et al carried out the path optimization research of a welding robot based on the ant colony optimization (ACO) algorithm and genetic algorithm (GA). The shortest path of the robot is optimized mainly using the ACO algorithm, and the shortest trajectory is optimized using GA. Simulation experiments show that the optimization strategy of ACO can increase the iterative calculation efficiency and path optimization performance of the algorithm [10]. However, in the process of path optimization, the energy loss and empty operation path of the welding robot are not considered.…”