Detecting contact when fingers are approaching an object and estimating the magnitude of the force the fingers are exerting on the object after contact are important tasks for a multi-fingered robotic hand to stably grasp objects. However, for a linkage-based under-actuated robotic hand with a self-locking mechanism to realize stable grasping without using external sensors, such tasks are difficult to perform when only analyzing the robot model or only applying data-driven methods. Therefore, in this paper, a hybrid of previous approaches is used to find a solution for realizing stable grasping with an under-actuated hand. First, data from the internal sensors of a robotic hand are collected during its operation. Subsequently, using the robot model to analyze the collected data, the differences between the model and real data are explained. From the analysis, novel data-driven-based algorithms, which can overcome noted challenges to detect contact between a fingertip and the object and estimate the fingertip forces in real-time, are introduced. The proposed methods are finally used in a stable grasp controller to control a triple-fingered under-actuated robotic hand to perform stable grasping. The results of the experiments are analyzed to show that the proposed algorithms work well for this task and can be further developed to be used for other future dexterous manipulation tasks.

中文翻译：

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无触觉传感器指尖接触检测和力估计，可实现欠驱动手的稳定抓握

当手指接近物体时检测接触并估计接触后手指对物体施加的力的大小是多指机械手稳定抓取物体的重要任务。然而，对于具有自锁机构的基于连杆的欠驱动机器人手来说，在不使用外部传感器的情况下实现稳定抓取，仅分析机器人模型或仅应用数据驱动方法很难执行此类任务。因此，在本文中，结合以前的方法来寻找一种解决方案，以实现欠驱动手的稳定抓取。首先，在机器人手操作过程中收集来自其内部传感器的数据。随后，利用机器人模型对收集到的数据进行分析，解释了模型与真实数据之间的差异。根据分析，引入了基于数据驱动的新颖算法，该算法可以克服检测指尖与物体之间的接触并实时估计指尖力的显着挑战。所提出的方法最终用于稳定抓取控制器中，以控制三指欠驱动机器人手执行稳定抓取。分析实验结果表明，所提出的算法非常适合该任务，并且可以进一步开发用于其他未来的灵巧操作任务。