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Trajectory planning method of robot sorting system based on S-shaped acceleration/deceleration algorithm

机译:基于S形加减速算法的机器人分拣系统轨迹规划方法

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To improve the sorting accuracy and efficiency of sorting system with large inertia robot, this article proposes a novel trajectory planning method based on S-shaped acceleration/deceleration algorithm. Firstly, a novel displacement segmentation method based on assumed maximum velocity is proposed to reduce the computational load of velocity planning. The sorting area can be divided into four parts by no more than three steps. Secondly, since the positions of workpieces are dynamically changing, a dynamic prediction method of workpiece picking position has been presented to consider all the possible positions of the robot and the workpiece, so as to realize the picking position prediction of the workpiece at any positions. Each situation in this method can constitute an equation with only one solution, and the existence of the solution can be verified by the proposed graphical method. The simulations of the motion time of the sorting process show that the proposed method can significantly shorten the sorting time and improve the sorting efficiency compared with the previous method. Finally, this method was applied to the Selective Compliance Assembly Robot Arm (SCARA) robot for experiments. In the physical picking experiment, the missing-pick rate was less than 1%, which demonstrates the efficiency and effectiveness of this method.
机译:为了提高大惯性机器人分拣系统的分拣精度和效率,提出了一种基于S形加减速算法的轨迹规划方法。首先,提出了一种基于假定最大速度的位移分割方法,以减少速度规划的计算量。分拣区域最多可分为三个部分,分为四个部分。其次,由于工件的位置是动态变化的,因此提出了一种工件拾取位置的动态预测方法,考虑了机器人和工件的所有可能位置,从而实现了工件在任意位置的拾取位置预测。该方法中的每种情况都可以仅用一个解决方案构成一个方程,并且可以通过提出的图形方法来验证该解决方案的存在。对分拣过程的运动时间进行了仿真,结果表明,与现有方法相比,该方法可以大大缩短分拣时间,提高分拣效率。最后,将该方法应用于选择性依从性装配机械臂(SCARA)机器人进行实验。在物理拣选实验中,遗漏率小于1%,证明了该方法的有效性。

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