SUMMARYOptimal grasping points for a robotic gripper were derived, based on object and hand geometry, using deep neural networks (DNNs). The optimal grasping cost functions were derived using probability density functions for each local cost function of the normal distribution. Using the DNN, the optimum height and width were set for the robot hand to grasp objects, whose geometric and mass centre points were also considered in obtaining the optimum grasping positions for the robot fingers and the object. The proposed algorithm was tested on 10 differently shaped objects and showed improved grip performance compared to conventional methods.

中文翻译：

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使用深度神经网络实现多个对象的稳定机器人抓取

总结基于物体和手的几何形状，使用深度神经网络 (DNN) 推导出了机器人抓手的最佳抓取点。使用正态分布的每个局部成本函数的概率密度函数得出最佳抓取成本函数。使用DNN，设置机器人手抓取物体的最佳高度和宽度，在获得机器人手指和物体的最佳抓取位置时，还考虑了其​​几何中心点和质心点。所提出的算法在 10 个不同形状的物体上进行了测试，与传统方法相比，其握持性能有所提高。