This article describes a set of control algorithms for in-hand object handling using a parallel jaw gripper equipped with force/tactile sensors. The control strategy is model based and relies upon the limit surface concept. The LuGre friction model is combined with the limit surface method to set up a dynamic model of soft contact. The model is exploited to estimate the relative velocity of the object with respect to the fingers, so as to control the grip force to counteract possible slipping events due to external disturbances. Force/tactile feedback, the only perception source used by the algorithms, is suitably exploited not only for safe grasping of a variety of objects with uncertain weight and inertial properties, but also for in-hand manipulation actions, like object pivoting or gripper pivoting. Since the algorithm is based on the control of the object velocity, accuracy of the desired object positioning depends on the initial grasp configuration, as well as on the accuracy of the friction model parameters. Such manipulation skills are evaluated in the execution of various pick and place tasks typical of an in-store logistic scenario.

中文翻译：

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基于力/触觉反馈的两指在手物体处理

本文介绍了一组控制算法，用于使用配备有力/触觉传感器的平行爪式抓手进行手持物体处理。控制策略基于模型并依赖于限制面概念。LuGre摩擦模型结合极限面法建立软接触动力学模型。该模型用于估计物体相对于手指的相对速度，从而控制抓握力以抵消由于外部干扰可能导致的滑动事件。力/触觉反馈是算法使用的唯一感知源，不仅适用于安全抓取具有不确定重量和惯性属性的各种物体，还适用于手部操纵动作，如物体旋转或抓手旋转。由于该算法基于对物体速度的控制，因此所需物体定位的精度取决于初始抓取配置以及摩擦模型参数的精度。此类操作技能在店内物流场景中典型的各种拾取和放置任务的执行中进行评估。