Constant-force curved-surface-tracking with robotic manipulator based on adaptive iterative algorithm
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摘要:
针对利用机器人进行打磨、抛光、去毛刺等场合时末端执行器对曲面工件轮廓跟踪时难以得到恒定接触力的问题，对机器人末端执行器和工件轮廓接触时的接触力进行研究，建立了实际跟踪过程中机器人末端执行器的接触力和已知传感器坐标系的映射关系，提出了一种基于自适应迭代学习算法的机器人力/位混合曲面恒力跟踪控制方法。该方法由两部分组成：基于机器人和环境接触时的阻抗模型设计了迭代学习控制律，在PD反馈控制的基础上通过迭代项克服机器人的未知参数和不确定性，并构建Lyapunov能量函数证明所提控制律的收敛性；将迭代学习控制律和力/位混合曲面恒力跟踪控制方法结合起来设计了用于曲面工件轮廓跟踪的控制方法。实验结果显示，经过15次迭代，接触力的波动范围逐渐变小并稳定在±3 N之内，验证了所提方法的有效性。
Abstract:This paper dealt with the fluctuation and instability of contact force that isgenerated between robot end-effector and environment during the process of robotic grinding, polishing and deburring. In order to obtain constant tracking force, the contact force is generated by robot end-effector onto the workpiece surface is analyzed and the mapping relationship between the contact force of robot end-effector in real contact conditions and the known sensor coordinate system was built. Meanwhile, a hybrid force/position control scheme based on adaptive iterative learning algorithm was proposed to compensate the robot end-effector trajectory offset. The control method is composed with two steps. An iterative learning control law was designed based on the impe-dance model of robot-environment in dynamic interaction task. This control law coped with the unknown parameters and disturbances by adding the iterative term to the PD feedback structure. Meanwhile, a Lyapunov energy function was designed to prove the convergence of the control law. The adaptive iterative learning control law was then combined with the force/position hybrid control method to design the constant-force curved-surface-tracking control scheme with robotic manipulator. The experimental results show that after 15 times iteration, the fluctuating range of contact force becomes small gradually and is within ±3 N, which illustrates the effectiveness of the designed control scheme.
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图 1 机器人曲面恒力跟踪实验平台模型
Figure 1. Experimental platform model of robot constant-force curved-surface-tracking
图 4 基于自适应迭代学习算法的力/位混合控制
Figure 4. Hybrid force/position control based on adaptive iterative learning algorithm
图 5 机器人曲面恒力跟踪实验平台
Figure 5. Experimental platform of robot constant-force curved-surface-tracking
图 9 迭代1次、7次和15次的跟踪过程
Figure 9. Tracking process after 1 iteration, 7 iterations and 15 iterations
图 10 工件曲线与实际跟踪曲线轨迹对比
Figure 10. Comparison between curve of workpiece and curve of tracking trajectory
图 11 没有迭代与迭代15次之后的跟踪曲线轨迹对比
Figure 11. Comparison between curve of tracking trajectory without iteration and curve of tracking trajectory after 15 iterations
表 1 误差分析
Table 1. Error analysis
迭代次数 接触力/N 误差绝对值平均值 误差标准差 误差方差 0 4.179 8 2.423 4 5.872 6 1 3.609 1 1.983 2 3.932 9 2 3.063 5 2.218 6 4.922 1 3 2.860 2 2.440 7 5.957 0 4 2.522 3 2.079 6 4.324 8 5 2.260 9 1.661 3 2.759 8 6 1.997 8 2.002 7 2.010 8 7 1.700 3 1.368 5 1.872 7 8 1.613 2 1.283 5 1.647 4 9 1.686 4 1.344 6 1.808 0 10 1.475 7 1.224 4 1.499 1 11 1.352 7 1.147 7 1.317 1 12 1.326 1 1.144 1 1.384 4 13 1.260 4 0.996 0 1.006 3 14 1.021 7 0.976 3 0.953 1 15 0.886 1 0.870 5 0.757 7 
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