论文标题
使用RGBD摄像头和力/扭矩传感的基于质量的质量稳健的姿势适应
Center-of-Mass-based Robust Grasp Pose Adaptation Using RGBD Camera and Force/Torque Sensing
论文作者
论文摘要
当机器人臂抓紧物体由于物体重力产生的其他矩，物体臂抓紧物体时，可能会掉落物体掉落。为了解决这个问题，我们提出了一项新颖的工作，该工作不需要额外的手腕和触觉传感器以及大量的学习实验。首先，我们使用机器人臂和RGBD摄像头上广泛固定的接头传感器获得了杆对象的质量位置。此外，我们给出了提高掌握稳定性的策略。模拟实验在“ Mujoco”中进行。结果表明，我们的工作有效地增强了稳健性。
Object dropping may occur when the robotic arm grasps objects with uneven mass distribution due to additional moments generated by objects' gravity. To solve this problem, we present a novel work that does not require extra wrist and tactile sensors and large amounts of experiments for learning. First, we obtain the center-of-mass position of the rod object using the widely fixed joint torque sensors on the robot arm and RGBD camera. Further, we give the strategy of grasping to improve grasp stability. Simulation experiments are performed in "Mujoco". Results demonstrate that our work is effective in enhancing grasping robustness.
