Purpose

The purpose of this paper is to improve the positional accuracy, smoothness on motion and productivity of industrial robot through the proposed optimal joint trajectory planning method. Also a new improved algorithm, i.e. non-dominated sorting genetic algorithm-II (NSGA-II) with achievement scalarizing function (ASF) has been proposed to obtain better optimal results compared to previously used optimization methods.

Design/methodology/approach

The end effector positional errors can be reduced by limiting the uncertainties of dynamic parameter variations like torque rate of joints. The jerk induced in robot joints due to acceleration variations are need to be minimized which otherwise induces vibrations in the manipulator that causes deviation in the encoders. But these lead to a vast increase in total travel time which affects the cost function of trajectory planning. Therefore, these three objectives need to be minimized individually so that an optimal trajectory path can be achieved with minimum positional error.

Findings

The simulation results have been obtained by running the proposed hybrid NSGA-II with ASF in MATLAB R2017a software. The optimal time intervals have been used to calculate jerk, acceleration and torque values for consecutive points on the trajectory path. From the simulation and experimental results, it can be concluded that the optimization technique could be used effectively for the trajectory planning of six-axis industrial manipulator in the joint space on the basis of minimum time-jerk-torque rate criteria.

Originality/value

In this paper, a new approach based on hybrid multi-objective optimization technique by combining NSGA-II with ASF has been applied to find the minimal time-jerk- torque rate joint trajectory of a six-axis industrial robot for obtaining higher positional accuracy. The results obtained from the execution of algorithm have been validated through experimentation using Kawasaki RS06L industrial robot for a particular defined path.

中文翻译：

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用于提高位置精度的工业机器人的最佳轨迹规划

目的

本文的目的是通过提出的最佳联合轨迹规划方法来提高工业机器人的位置精度，运动的平滑度和生产率。还提出了一种新的改进算法，即具有成就标量函数（ASF）的非主导排序遗传算法II（NSGA-II），与以前使用的优化方法相比，可以获得更好的最优结果。

设计/方法/方法

可以通过限制动态参数变化（如关节的扭矩率）的不确定性来减少末端执行器的位置误差。需要将由于加速度变化而在机器人关节中引起的跳动减至最小，否则会在机械手中引起振动，从而导致编码器产生偏差。但是，这些导致总的旅行时间大大增加，从而影响了轨迹规划的成本函数。因此，这三个目标需要分别最小化，以便可以以最小的位置误差实现最佳的轨迹路径。

发现

通过在MATLAB R2017a软件中运行带有ASF的拟议混合NSGA-II获得了仿真结果。最佳时间间隔已用于计算轨迹路径上连续点的加速度，加速度和扭矩值。从仿真和实验结果可以看出，该优化技术可以在最小时变转矩率准则的基础上，有效地用于关节空间内六轴工业机械手的轨迹规划。

创意/价值

本文提出了一种基于混合多目标优化技术的新方法，该方法将NSGA-II与ASF相结合，从而找到了六轴工业机器人的最小时间抖动-扭矩率关节轨迹，以获得更高的定位精度。通过执行川崎RS06L工业机器人针对特定定义路径进行的实验，验证了从算法执行中获得的结果。