摘要
针对工业应用中并联机器人运动规划与控制的精度需求,提出一种基于多样性反馈的自适应粒子群优化算法(APSO)来提高并联机器人精度。利用机器人机构闭环矢量关系得到运动学正逆解;并应用基于多样性反馈的自适应粒子群优化算法将结构参数优化问题转化为非线性系统优化问题。通过对机器人驱动电机转角进行优化,使得机器人在工作空间内具有理想的工作精度。以SRBD型号Delta型并联机器人进行试验验证,仿真结果可达到10^(-3)毫米级别,验证了方法的有效性用。
For the accuracy requirement of the motion planning and control in the industrial application of parallel robot, an particle swarm optimization algorithm based was proposed on adaptive diversity feedback （APSO） to improve the accuracy of parallel robots.Use the closed loop vector to calculate the robot＆#39;s kinematics solution, and the application of APSO turn the structure parameters optimization problem into an optimization problem of nonlinear system, by calibrate the robot drive motor rotation, the machine get an ideal accuracy in the working space.Test on SRBD Delta parallel robot, and the simulation results can reach the level of 10-3 mm, the validity of the method is verified.
出处
《科学技术与工程》
北大核心
2017年第20期1-5,共5页
Science Technology and Engineering
基金
沈阳市信息产品制造业发展专项资金(Z201401001)资助
关键词
并联机器人
装配误差
粒子群优化算法
误差补偿
parallel robot
assembly error
particle swarm optimization algorithm
error calibration
