TY - GEN
T1 - An effective trajectory optimization method for robotic belt grinding based on intelligent algorithm
AU - Liang, Wei
AU - Song, Yixu
AU - Lv, Hongbo
AU - Jia, Peifa
AU - Gan, Zhongxue
AU - Qi, Lizhe
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
KW - Cooperative optimization
KW - Dynamic model
KW - Particle swarm optimization
KW - Robotic belt grinding
KW - Support vector machine
KW - Trajectory optimization
KW - Trajectory planning
UR - https://www.scopus.com/pages/publications/79952947659
U2 - 10.1109/ROBIO.2010.5723489
DO - 10.1109/ROBIO.2010.5723489
M3 - Conference contribution
AN - SCOPUS:79952947659
SN - 9781424493173
T3 - 2010 IEEE International Conference on Robotics and Biomimetics, ROBIO 2010
SP - 1142
EP - 1147
BT - 2010 IEEE International Conference on Robotics and Biomimetics, ROBIO 2010
PB - IEEE Computer Society
ER -