Self-Contained and Automatic Calibration of a Multi-Fingered Hand Using Only Pairwise Contact Measurements

A self-contained calibration procedure that can be performed automatically without additional external sensors or tools is a significant advantage, especially for complex robotic systems. Here, we show that the kinematics of a multi-fingered robotic hand can be precisely calibrated only by moving the tips of the fingers pairwise into contact. The only prerequisite for this is sensitive contact detection, e.g., by torque-sensing in the joints (as in our DLR-Hand II) or tactile skin. The measurement function for a given joint configuration is the distance between the modeled fingertip geometries, but the actual measurement is always zero. In an in-depth analysis, we prove that this contact-based calibration determines all quantities needed for manipulating objects with the hand, i.e., the difference vectors of the fingertips, and that it is as sensitive as a calibration using an external visual tracking system and markers. We describe the complete calibration scheme, including the selection of optimal sample joint configurations and search motions for the contacts despite the initial kinematic uncertainties. In a real-world calibration experiment for the torque-controlled four-fingered DLR-Hand II, the maximal error of 17.7mm can be reduced to only 3.7mm.Comment: Presented at the 2023 IEEE-RAS International Conference on Humanoid Robot

Haptic object recognition using a multi-fingered dextrous hand

The use of a dextrous, multifingered hand for high-level object recognition tasks is considered. The paradigm is model-based recognition in which the objects are modeled and recovered as superquadratics, which are shown to have a number of important attributes that make them well suited for such a task. Experiments have been performed to recover the shape of objects using sparse contacts point data from the hand with promising results. The authors also propose an approach to using tactile data in conjunction with the dextrous hand to build a library of grasping and exploration primitives that can be used in recognizing and grasping more complex multipart objects

A survey of dextrous manipulation

technical reportThe development of mechanical end effectors capable of dextrous manipulation is a rapidly growing and quite successful field of research. It has in some sense put the focus on control issues, in particular, how to control these remarkably humanlike manipulators to perform the deft movement that we take for granted in the human hand. The kinematic and control issues surrounding manipulation research are clouded by more basic concerns such as: what is the goal of a manipulation system, is the anthropomorphic or functional design methodology appropriate, and to what degree does the control of the manipulator depend on other sensory systems. This paper examines the potential of creating a general purpose, anthropomorphically motivated, dextrous manipulation system. The discussion will focus on features of the human hand that permit its general usefulness as a manipulator. A survey of machinery designed to emulate these capabilities is presented. Finally, the tasks of grasping and manipulation are examined from the control standpoint to suggest a control paradigm which is descriptive, yet flexible and computationally efficient1

Haptic Perception with a Robot Hand: Requirements and Realization

This paper first discusses briefly some of the recent ideas of perceptual psychology on the human haptic system particularly those of J.J. Gibson and Klatzky and Lederman. Following this introduction, we present some of the requirements of robotic haptic sensing and the results of experiments using a Utah/MIT dexterous robot hand to derive geometric object information using active sensing

The Role of Learning and Kinematic Features in Dexterous Manipulation: a Comparative Study with Two Robotic Hands

Dexterous movements performed by the human hand are by far more sophisticated than those achieved by current humanoid robotic hands and systems used to control them. This work aims at providing a contribution in order to overcome this gap by proposing a bio-inspired control architecture that captures two key elements underlying human dexterity. The first is the progressive development of skilful control, often starting from – or involving – cyclic movements, based on trial-and-error learning processes and central pattern generators. The second element is the exploitation of a particular kinematic features of the human hand, i.e. the thumb opposition. The architecture is tested with two simulated robotic hands having different kinematic features and engaged in rotating spheres, cylinders, and cubes of different sizes. The results support the feasibility of the proposed approach and show the potential of the model to allow a better understanding of the control mechanisms and kinematic principles underlying human dexterity and make them transferable to anthropomorphic robotic hands

The role of learning and kinematic features in dexterous manipulation: a comparative study with two robotic hands

Dexterous movements performed by the human hand are by far more sophisticated than those achieved by current humanoid robotic hands and systems used to control them. This work aims at providing a contribution in order to overcome this gap by proposing a bio-inspired control architecture that captures two key elements underlying human dexterity. The first is the progressive development of skilful control, often starting from - or involving - cyclic movements, based on trial-and-error learning processes and central pattern generators. The second element is the exploitation of a particular kinematic features of the human hand, i.e. the thumb opposition. The architecture is tested with two simulated robotic hands having different kinematic features and engaged in rotating spheres, cylinders, and cubes of different sizes. The results support the feasibility of the proposed approach and show the potential of the model to allow a better understanding of the control mechanisms and kinematic principles underlying human dexterity and make them transferable to anthropomorphic robotic hands

A variable stiffness soft gripper using granular jamming and biologically inspired pneumatic muscles

As the domains in which robots operate change the objects a robot may be required to grasp and manipulate are likely to vary significantly and often. Furthermore there is increasing likelihood that in the future robots will work collaboratively alongside people. There has therefore been interest in the development of biologically inspired robot designs which take inspiration from nature. This paper presents the design and testing of a variable stiffness, three fingered soft gripper which uses pneumatic muscles to actuate the fingers and granular jamming to vary their stiffness. This gripper is able to adjust its stiffness depending upon how fragile/deformable the object being grasped is. It is also lightweight and low inertia making it better suited to operation near people. Each finger is formed from a cylindrical rubber bladder filled with a granular material. It is shown how decreasing the pressure inside the finger increases the jamming effect and raises finger stiffness. The paper shows experimentally how the finger stiffness can be increased from 21 to 71 N/m. The paper also describes the kinematics of the fingers and demonstrates how they can be position-controlled at a range of different stiffness values

Performance of modified jatropha oil in combination with hexagonal boron nitride particles as a bio-based lubricant for green machining

This study evaluates the machining performance of newly developed modified jatropha oils (MJO1, MJO3 and MJO5), both with and without hexagonal boron nitride (hBN) particles (ranging between 0.05 and 0.5 wt%) during turning of AISI 1045 using minimum quantity lubrication (MQL). The experimental results indicated that, viscosity improved with the increase in MJOs molar ratio and hBN concentration. Excellent tribological behaviours is found to correlated with a better machining performance were achieved by MJO5a with 0.05 wt%. The MJO5a sample showed the lowest values of cutting force, cutting temperature and surface roughness, with a prolonged tool life and less tool wear, qualifying itself to be a potential alternative to the synthetic ester, with regard to the environmental concern

Structured manifolds for motion production and segmentation : a structured Kernel Regression approach

Steffen JF. Structured manifolds for motion production and segmentation : a structured Kernel Regression approach. Bielefeld (Germany): Bielefeld University; 2010