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Error regulation strategies for Model Based visual servoing tasks: Application to autonomous object grasping with Nao robot

机译:基于模型的视觉伺服任务的错误调节策略:在Nao机器人自主抓取物体中的应用

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When applying service robotic tasks using sensor based control, a classical exponential decrease of the error is usually used in the control laws which can reduces the performance of the executed task. In fact, due to this choice, the convergence time greatly increases especially at the end of the process. To ameliorate the performance of such tasks, we present in this paper two new error regulation strategies to accelerate the service tasks execution. These propositions are compared with the classical one in the case of performing autonomous object's manipulation tasks using real-time visual servoing. The Model Based Tracking method is used to apply head servoing and grasping of different objects using Nao humanoid robot.
机译:当使用基于传感器的控制来应用服务机器人任务时,通常会在控制定律中使用经典的指数减少误差,这会降低已执行任务的性能。实际上,由于这种选择,收敛时间大大增加,尤其是在过程结束时。为了改善此类任务的性能,我们在本文中提出了两种新的错误调整策略,以加快服务任务的执行速度。在使用实时视觉伺服执行自主对象的操纵任务的情况下,将这些命题与经典命题进行了比较。基于模型的跟踪方法用于使用Nao人形机器人对头部进行伺服和抓取不同物体。

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