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Self-Calibration for an Object Integrative Handling System

机译:对象一体化处理系统的自校准

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The cooperative manipulation of rigid objects with multiple robotic arms leads to redundant actuation, which is a challenging configuration for the robot control. The object is connected to multiple robotic arms and integrated into the kinematic structure, resulting in a Re-Configuration for each handling task. A recent example of this object integration is the investigated PARARIP handling system. Inaccuracies of the robotic arms and tolerances of the object lead to deviations of the grasp points at the object and inadequate object positioning. Today's control and calibration approaches, however, do not target the identification of the uncertain grasp points. Accordingly, a self-calibration procedure for the object integrative handling system is developed to identify the actual grasp points at the object. It is based on the combination of direct and inverse kinematic calculations. The aim of the paper is to evaluate redundant sensor-information of cooperating robotic arms to identify the actual grasp points at the integrated object, to increase the system accuracy and to limit the impact of the coupling effects. The sensitivity and influencing factors of the method were investigated and verified in simulations. The results show that the available redundant sensor information for object integrative robots or handling systems can be used to identify the grasp points at the object and compensate the internal inaccuracies automatically.
机译:具有多个机器人臂的刚性物体的合作操纵导致冗余致动,这是机器人控制的具有挑战性的配置。该物体连接到多个机器人臂并集成到运动结构中,导致每个处理任务的重新配置。最近这个对象集成的示例是调查的伞子处理系统。机器人臂的不准确性和物体的公差导致抓握点处的夹具处于物体和物体定位不足的偏差。然而,今天的控制和校准方法不瞄准不确定掌握点的识别。因此,开发了对对象综合处理系统的自校准过程以识别对象处的实际掌握点。它基于直接和逆运动学计算的组合。本文的目的是评估合作机器人臂的冗余传感器信息,以识别集成对象的实际掌握点,以提高系统精度并限制耦合效果的影响。研究和验证了该方法的灵敏度和影响因素。结果表明,对象一体化机器人或处理系统的可用冗余传感器信息可用于识别对象处的掌握点,并自动补偿内部不准确性。

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