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Robust Robotic Grasping Force Optimization with Uncertainty

机译:强大的机器人抓斗力优化不确定性

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The contact location and the coefficient of friction are two of the main uncertainties that rise in the robotic grasping force analysis problem. In many robotic tasks, we seek robust solutions to problem, where the problem data suffer from known statistical uncertainty, that is, a solution minimizes the objective function for the worst-case while satisfying the constraints for every possible case. Our objective is to reveal the effect of the uncertainty on the optimal solution. First we propose an uncertain point contact model (UPCM), which can describe the contact location uncertainty. Then together with the robotic grasping constraints, we formulate the constrained grasping force optimization problem as an unconstrained least-squares problem based on exterior penalty method. Finally, a numerical example is given to illustrate the method.
机译:接触位置和摩擦系数是机器人抓握力分析问题中升高的两个主要不确定性。在许多机器人任务中,我们寻求强大的问题解决问题,其中问题数据遭受已知的统计不确定性,即,解决方案最小化最坏情况的目标函数,同时满足每个可能的情况的约束。我们的目标是揭示不确定性对最佳解决方案的影响。首先,我们提出了一个不确定的点接触模型(UPCM),其可以描述联系地点不确定性。然后与机器人抓握约束一起,我们根据外部惩罚方法制定受约束抓力优化问题作为不受约束的最小二乘问题。最后,给出了一个数值例子来说明该方法。

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