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

Abstract

In order to improve the welding efficiency of the ship welding robot, the path planning of the welding robot based on immune optimization is proposed by taking the welding path length and energy loss as the optimization goals. First, on the basis of the definition of the path planning of the welding robot, the grid modeling of the robot’s working environment and the triangular modeling of the welding weldments are carried out. Then, according to the working process of the welding robot, the length objective function, including the welded seam path and the welding torch path without welding, is constructed, and the energy loss function is constructed based on the kinematics and Lagrange function. Finally, the immune optimization algorithm based on cluster analysis and self-circulation is introduced to realize the multi-objective optimization of the path planning for the ship welding robot. The test results of four kinds of ship welding weldments show that compared with the simple genetic algorithm, immune genetic algorithm, ant colony algorithm, artificial bee colony, particle swarm optimization, and immune cloning optimization, the proposed multi-objective immune planning algorithm is the best in terms of planning path length, energy consumption, and stability. Furthermore, the average shortest path and its standard deviation, the average minimum energy consumption and its standard deviation, and the average lowest convergence generation and its standard deviation are reduced by an average of 9.03%, 54.04%, 8.23%, 19.10%, 27.84%, and 52.25%, respectively, which fully verifies the effectiveness and superiority of the proposed welding robot path planning algorithm.

Task Parameter Planning Algorithm for UAV Area Complete Coverage in EO Sector Scanning Mode

“…Break out of the loop; 10: End if 11: End for For any set (V, h, θ 1 , ω) in the IA population, with a complete coverage width d 1 , the J index can be generated based on Equation (45), which can be used as the fitness of the set.…”

“…In the actual task process, not only is high search efficiency required, but many other factors must also be considered. The task planning simulation was performed according to the comprehensive optimization index J established by Equation (45). Set K η = 100,…”

“…The Genetic Algorithm (GA) [44] is suitable for almost all optimization problems, but requires a large amount of computation and is generally used in offline situations. The Immune Algorithm (IA) [45] retains the evolutionary mechanism of GA, and at the same time has a unique concentration inhibition mechanism, which better overcomes the problem of local optimization, but has the disadvantage of high computational cost. The Variable Neighborhood Search (VNS) [46] and Multistage Neighborhood Search (MNS) [47] algorithms overcome the local search limitation of Neighborhood Search (NS) and can search the solution space in multiple different neighborhoods, so they have good global search ability.…”