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

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...

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Vydáno v:Electronics (Basel) Ročník 12; číslo 9; s. 2040
Hlavní autoři: Shen, Yi, Gao, Yunqiang, Yuan, Mingxin, Sun, Hongwei, Guo, Zhenjie
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 28.04.2023
Témata:
Algorithms
Ant colony optimization
Cloning
Cluster analysis
Efficiency
Energy consumption
Environment models
Genetic algorithms
Kinematics
Mathematical models
Mathematical optimization
Multiple objective analysis
Optimization algorithms
Particle swarm optimization
Robot dynamics
Robots
Shipbuilding
Shortest-path problems
Simulation
Standard deviation
Technology application
Traveling salesman problem
Welding
Weldments
Working conditions
China
ISSN:2079-9292, 2079-9292
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Shrnutí: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.
Bibliografie:ObjectType-Article-1
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content type line 14
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics12092040