Name
Title | ||
|---|---|---|
An effective trajectory optimization method for robotic belt grinding based on intelligent algorithm. |
Abstract | ||
|---|---|---|
In this paper, a novel method for robotic belt grinding based on support vector machine and particle swarm optimization algorithm is presented. Firstly, the dynamic model of the robotic belt grinding process is built using support vector machine method. This is the basis of our work because the dynamic model shows the relation between the removal and control parameters (contact force and robot's speed) of robot. Secondly, the method of reverse solution of the dynamic model is introduced. According to this method, control parameters of robot can be accurately calculated by the given value of removal. Thirdly, the standard PSO algorithm is introduced to get smooth and stable trajectories of the control parameters, because the trajectory jitter of the control parameters has a great influence on the grinding accuracy. Finally, a variation on the traditional PSO algorithm is presented, which is called the cooperative particle swarm optimizer, or CPSO, employing cooperative behavior to significantly improve the performance of the original algorithm. The experiment results show that the novel method for robotic belt grinding performs well in the control of the robot parameters and the grinding accuracy and efficiency is improved. © 2010 IEEE. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ROBIO.2010.5723489 | ROBIO |
Keywords | Field | DocType |
cooperative optimization,dynamic model,particle swarm optimization,robotic belt grinding,support vector machine,trajectory optimization,trajectory planning,support vector machines,mathematical model,trajectory,optimization,mobile robots | Particle swarm optimization,Trajectory optimization,Control theory,Support vector machine,Algorithm,Control engineering,Engineering,Belt grinding,Robot,Grinding,Trajectory,Mobile robot | Conference |
Volume | Issue | Citations |
null | null | 0 |
PageRank | References | Authors |
0.34 | 2 | 6 |