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Research on the inverse kinematics solution of robot arm for watermelon picking

机译:西瓜采摘机械臂逆运动学研究

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This paper puts forward a genetic algorithm based on non-linear programming in order to deal with the inverse kinematics solution precision of robot arm for watermelon picking, ensure the yield after picking and improve the fruit quality after picking. The robot arm for watermelon picking adopts the model of Denavit-Hartenberg, which mainly applies the non-linear genetic algorithm to give solution on the inverse kinematics issues. Lastly the paper differentiate the obtained inverse kinematics parameters through random forests algorithm. This paper respectively applies the genetic algorithm and nonlinear programming genetic algorithm for inverse kinematics solution on robot arm for watermelon picking with five degrees, six degrees and seven degrees of freedom. The experiment result shows that the non-linear programming genetic algorithm could effectively give inverse kinematics solution on robot arm for watermelon picking with multiple degrees of freedom, with the solution precision 300 to 600 times that of the genetic algorithm. There are more than one solution for the inversion result of the robot arm and the random forests algorithm could select fairly good picking path and gesture to reduce the unnecessary damage to the watermelon fruits in the picking.
机译:提出了一种基于非线性规划的遗传算法,以解决西瓜采摘机械臂逆运动学求解精度问题,保证采摘后的产量,提高采摘后的果实品质。西瓜采摘机械臂采用Denavit-Hartenberg模型,主要采用非线性遗传算法对逆运动学问题进行求解。最后,本文通过随机森林算法对获得的逆运动学参数进行了区分。本文分别采用遗传算法和非线性规划遗传算法对五自由度,六自由度和七自由度的西瓜采摘机器人手臂进行逆运动学求解。实验结果表明,该非线性规划遗传算法可以有效地给出多自由度采摘西瓜机械臂的逆运动学解,其求解精度是遗传算法的300〜600倍。对于机械臂的反演结果,有多种解决方案,随机森林算法可以选择相当好的采摘路径和手势,以减少采摘过程中对西瓜果实的不必要损害。

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