Path Planning Methodology for Multi-Layer Welding of Intersecting Pipes Considering Collision Avoidance

Abstract

SUMMARY The V-groove joint of thick wall intersecting pipes must be filled by multi-layer weld. The welding path of intersecting pipes is complicated, and hence multi-layer welds increase the complexity of the problem. This paper proposes a methodology for path planning of multi-layer weld of thick wall intersecting pipes. The methodology is based on measuring the electrode pose located in both side and front views of intersecting pipes. In order to compensate for the path deviation around the pipe circumference, the measured values are used to interpolate the path of each pass between two views. The methodology has been applied in a case study. Simulation results approve that multi-layer weld appropriately fills the V-groove joint space around the pipe circumference. In addition, collision avoidance between welding torch and pipes is considered by introducing a safety ring. While the robot wrist moves inside the safety ring, no collision occurs. Simulation results show the robustness of the proposed path planning method, introduced for collision avoidance.

Comparison of different sample-based motion planning methods in redundant robotic manipulators

“…The first term Xs indicates the robot base position in centimeter, while other six terms, that is, [1,2,3,4,5,6] indicate manipulator joints angles in radians. For goal state, a general default tolerance of 1e-5 length unit in position and 1e-5 radians in orientation is specified in Moveit.…”

“…In many applications, like spot welding or multi-position welding in a large workspace, it is necessary that a robotic manipulator find a collision-free path to move from an earlier working point to the new working point [1][2][3]. The goal of motion planning in these cases is to find a collision-free path.…”

Collision-Free Motion Planning for an Aligned Multiple-turret System Operating in Extreme Environment

“…After obtaining the C-Space, the rest is related to path planning algorithms which can be A* [34,35], many variants of rapidly exploring random tree (RRT) [36], probabilistic roadmap (PRM) [37], particle swarm optimization (PSO) [38], and so forth. Apart from these, some papers published recently have also provided guidance [39,40,41,42,43]. In this study, A* path planning algorithm will be used.…”

Path planning for robots in preform weaving based on learning from demonstration

“…With the rise of complex application environments for robots, tasks like polishing [25], welding [26], and playing table tennis [27] have become more difficult, requiring improved path planning. To address this, researchers have explored learning from demonstration (LfD) methods that can learn task actions and skill features from manual demonstrations.…”