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Keywords Welding robot Path planning Particle swarm optimization Genetic algorithm
4.1 Introduction
Welding is an important part in industrial manufacturing, and welding robots are widely used in automotive, aerospace, machinery, and processing industries. With the extensive application of welding robots, more and more researches on welding robot technology have been conducted. Spot welding robot path planning is ben- eficial to welding process, especially when many welding joints existed. A rea- sonable welding path for welding robot can shorten the overall time, improve production efficiency, and reduce production costs. Otherwise, it would be time consuming when the welding path is not reasonable. The development of intelligent
X.W. Wang (&) · Y.P. Shi · R. Yu · X.S. Gu
School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
e-mail: wangxuew@ecust.edu.cn
© Springer-Verlag Berlin Heidelberg 2015 25
Z. Deng and H. Li (eds.), Proceedings of the 2015 Chinese Intelligent Automation Conference, Lecture Notes in Electrical Engineering 337, DOI 10.1007/978-3-662-46463-2_4
algorithm provides effective methods for solving path planning of welding robot problem. Currently, GA and PSO algorithms are effective methods used to solve the problem of robot path planning [1, 2].
The particle swarm optimization (PSO) algorithm is simple to implement and has less parameters [3]; hence, it has been widely used for solving both continuous optimization and discrete optimization problems in recent years, such as drilling sequence optimization for PCB circuit boards and traveling salesman problem (TSP) [1, 4]. Genetic algorithm (GA) was presented in the early 1960 by John Holland in the University of Michigan. It is a heuristic searching algorithm that mimics the process of natural selection. As a global optimization algorithm, GA simulates natural genetic selection in biological evolution and natural selection probability [5]. The idea of GA originated from Darwin’s evolution theory and Mendelian inheri- tance, which simulates the mechanisms of biological evolution to construct an algorithm’s iterative process. It is already used in solving the multiple welding joints optimization problems [2, 6]. In this paper, hybrid PSO is presented in Sect. 4.2 first. Then, improved PSO algorithms are presented to solve welding robot path planning problem in Sect. 4.3. At last, the conclusion is given in Sect. 4.4.
4.2 Hybrid Particle Swarm Optimization
4.2.1 GA-PSO
The persity of particles in PSO can be improved after GA was combined due to its mutation characteristic, then the global search capability of PSO will be improved too. In Ref. [7], GA was used to initialize the particles in PSO to solve the design of a diesel engine combustion chamber, and the simulation results showed its effec- tiveness. However, GA is easy to fall into local optimum for large dimensional problem. The selection and crossover operations in GA were improved to ensure the persity of particles, and a good result was obtained for the global optimization of multimodal functions [8]. In Ref. [9], the iteration of particles is updated based on probability operation of GA to realize discrete PSO application. In Ref. [10], a hybrid GA-PSO based on clustering algorithm was proposed to optimize the scheduling problem in the computer industry. Based on the above researches, a double global optimum GA-PSO algorithm is proposed to solve welding robot path planning problem in this paper.
In the particle iteration, each particle is updated based on the personal best (pbest) and global best particle (gbest). Then the ultimate solution converges to optimal or suboptimal because the particles are always updated based on pbest and gbest. But when the pbest and gbest fell into local optimal solution, the particle cannot jump out of the local optimal solutions either.