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Hand–eye calibration and grasping pose calculation with motion error compensation and vertical-component correction for 4-R(2-SS) parallel robot

机译:用运动误差补偿和4-R(2-SS)并联机器人的运动误差补偿和垂直分量校正的手眼校准和抓握姿态计算

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Due to motion constraint of 4-R(2-SS) parallel robot, it is difficult to calculate the translation component of hand–eye calibration based on the existing model solving method accurately. Additionally, the camera calibration error, robot motion error, and invalid calibration motion poses make it difficult to achieve fast and accurate online hand–eye calibration. Therefore, we propose a hand–eye calibration method with motion error compensation and vertical-component correction for 4-R(2-SS) parallel robot by improving the existing eye-to-hand model and solving method. Firstly, the eye-to-hand model of single camera is improved and the robot motion error in the improved model is compensated to reduce the influence of camera calibration error and robot motion error on model accuracy. Secondly, the vertical-component of hand–eye calibration is corrected based on vertical constraint between calibration plate and end effector in parallel robot to calculate the pose and motion error in calibration of 4-R(2-SS) parallel robot accurately. Thirdly, the nontrivial solution constraint of eye-to-hand model is constructed and adopted to remove invalid calibration motion poses and plan calibration motion. Finally, the proposed method was verified by experiments with a fruit sorting system based on 4-R(2-SS) parallel robot. Compared with random motion, the existing model, and solving method, the average time of online calibration based on planned motion decreases by 29.773 s and the average error of calibration based on the improved model and solving method decreases by 151.293. The proposed method can improve the accuracy and efficiency of hand–eye calibration of 4-R(2-SS) parallel robot effectively and further realize accurate and fast grasping.
机译:由于4-R(2-SS)并联机器人的运动约束,难以基于现有的模型求解方法来计算手眼校准的平移组件。此外,相机校准误差,机器人运动错误和无效的校准运动姿势使得难以实现快速准确的在线手眼校准。因此,我们通过改进现有的令人眼光模型和求解方法,提出一种手眼校准方法,对于4-R(2-SS)并联机器人,具有运动误差补偿和垂直分量校正。首先,改善了单个摄像机的眼睛到手动模型,并补偿了改进模型中的机器人运动误差,以减少相机校准误差和机器人运动误差对模型精度的影响。其次,基于并联机器人的校准板和末端执行器之间的垂直约束来校正手眼校准的垂直分量,以准确地计算校准4-R(2-SS)并联机器人的姿势和运动误差。第三,构造和采用了令人眼光模型的非竞争解决方案约束以去除无效的校准运动姿势和计划校准运动。最后,通过基于4-R(2-SS)并联机器人的水果分选系统的实验验证了所提出的方法。与随机运动相比,现有模型和求解方法,基于计划运动的在线校准的平均时间减少了29.77秒,并且基于改进的模型和求解方法的校准平均误差减少了151.293。所提出的方法可以有效地提高4-R(2-SS)并联机器人的手眼校准的准确性和效率,并进一步实现精确且快速抓握。

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