## 1 Introduction  
With developments in welding technology, computer technology and robot technology, welding robots have been widely used in industrial production, especially in the automotive sector. To improve welding productivity when there are many weld joints, robot path planning needs to be studied. For welding robot path planning, some factors should be considered. Optimization objectives include a minimum path length [1], collision-free paths [2], and welding deformation [3].  
Obstacle avoidance is a fundamental problem in welding robot path planning. However, most path planning with obstacle avoidance is obtained based on teaching mode programming. In this way, the planning process is time-consuming, and the optimum path is hard to achieve. Hence, robot collision-free path planning based on optimization algorithms needs to be studied to improve welding efficiency. Collision-free path planning includes two stages: environment modeling and path searching, which are explained in the following paragraphs.  
Environment modeling refers to the mathematical description of the environment around the welding robot, which is essential for collision-free path planning. [PERSON] [4] applied a grid method to solve path planning for real-time robots in dynamic environments. For the grid method, grids are applied to describe the free space and obstacle space in the robot's work space. A good path can be achieved if the precision is high, but the algorithm would be time-consuming at the same time. The environment would not be described clearly if the precision were otherwise low. [PERSON] [5] presented a C-space layered search arithmetic for manipulators. Although the method could achieve obstacle avoidance for robot arms, the obstacle modeling was complex and inefficient. With increases in robot freedom, the computation becomes very complicated. In addition, there are other environmental modeling methods, such as the visibility graph method [6], the bounding box method [7], artificial neural networks [8], and approximate voronoi boundary networks [9].  
After environment modeling, some path searching methods are essential to find an appropriate path. [PERSON] [10] presented an improved APF-based SIFORS method for autonomous mobile robot path planning in complex environments. The artificial potential field method is straightforward, but some problems may exist, such as the local optimum problem, falling into a deadlock, and moving unsteadily near the obstacle. [PERSON] [11] proposed an improved ant colony algorithm for robot global path planning: the pheromone evaporation rate was adjusted dynamically to enhance the global search ability and convergence speed. In addition, the artificial bee colony algorithm [12] and the genetic algorithm [13] are also used to search the optimum path.  
In this paper, obstacle avoidance and path length are considered to optimize path. Based on the above analysis, the model description is first described in Section 2. Then, in Section 3, the grid method is selected to realize environment modeling. Moreover, the ant colony algorithm (ACO) is applied for path searching in Section 4. In addition to this, a particle warm optimization (PSO) algorithm is used to realize global optimization in Section 5. Finally, conclusions are given in Section 6.