An introduction to dexterous manipulation in anthromorphic robotic hands

2021 ◽  
Author(s):  
Danielle Castley
Keyword(s):  
Robotic Hands ◽  
Dexterous Manipulation

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

10.5772/56479 ◽  
2013 ◽  
Vol 10 (10) ◽  
pp. 340 ◽  
Author(s):  
Anna Lisa Ciancio ◽  
Loredana Zollo ◽  
Gianluca Baldassarre ◽  
Daniele Caligiore ◽  
Eugenio Guglielmelli
Keyword(s):  
Comparative Study ◽  
Robotic Hands ◽  
Dexterous Manipulation

Automated Design of Robotic Hands for In-Hand Manipulation Tasks

2020 ◽  
Vol 17 (01) ◽  
pp. 1950029
Author(s):  
Christopher Hazard ◽  
Nancy Pollard ◽  
Stelian Coros
Keyword(s):  
Kinematic Model ◽  
Automated Design ◽  
Robotic Hand ◽  
Motion Synthesis ◽  
Robotic Hands ◽  
Dexterous Manipulation ◽  
Grasp Planning ◽  
User Input ◽  
High Level

Grasp planning and motion synthesis for dexterous manipulation tasks are traditionally done given a pre-existing kinematic model for the robotic hand. In this paper, we introduce a framework for automatically designing hand topologies best suited for manipulation tasks given high-level objectives as input. Our pipeline is capable of building custom hand designs around specific manipulation tasks based on high-level user input. Our framework comprises of a sequence of trajectory optimizations chained together to translate a sequence of objective poses into an optimized hand mechanism along with a physically feasible motion plan involving both the constructed hand and the object. We demonstrate the feasibility of this approach by synthesizing a series of hand designs optimized to perform specified in-hand manipulation tasks of varying difficulty. We extend our original pipeline 32 to accommodate the construction of hands suitable for multiple distinct manipulation tasks as well as provide an in depth discussion of the effects of each non-trivial optimization term.

scholarly journals Estimating Fingertip Forces, Torques, and Local Curvatures from Fingernail Images

Robotica ◽  
2019 ◽  
Vol 38 (7) ◽  
pp. 1242-1262 ◽  
Author(s):  
Nutan Chen ◽  
Göran Westling ◽  
Benoni B. Edin ◽  
Patrick van der Smagt
Keyword(s):  
Neural Networks ◽  
Contact Surface ◽  
Force Estimation ◽  
Robotic Hands ◽  
Dexterous Manipulation ◽  
Color Distribution ◽  
Experimental Environment ◽  
Single Finger ◽  
Fingertip Forces ◽  
Surrounding Skin

SUMMARYThe study of dexterous manipulation has provided important insights into human sensorimotor control as well as inspiration for manipulation strategies in robotic hands. Previous work focused on experimental environment with restrictions. Here, we describe a method using the deformation and color distribution of the fingernail and its surrounding skin to estimate the fingertip forces, torques, and contact surface curvatures for various objects, including the shape and material of the contact surfaces and the weight of the objects. The proposed method circumvents limitations associated with sensorized objects, gloves, or fixed contact surface type. In addition, compared with previous single finger estimation in an experimental environment, we extend the approach to multiple finger force estimation, which can be used for applications such as human grasping analysis. Four algorithms are used, c.q., Gaussian process, convolutional neural networks, neural networks with fast dropout, and recurrent neural networks with fast dropout, to model a mapping from images to the corresponding labels. The results further show that the proposed method has high accuracy to predict force, torque, and contact surface.

Control of a Biomimetic Robot Hand Finger

2015 ◽  
pp. 475-499
Author(s):  
Yunus Ziya Arslan ◽  
Yuksel Hacioglu ◽  
Yener Taskin ◽  
Nurkan Yagiz
Keyword(s):  
Sliding Mode ◽  
Control Strategies ◽  
Metabolic Energy ◽  
Human Hand ◽  
Robot Hand ◽  
Robotic Hands ◽  
Dexterous Manipulation ◽  
Model And Simulation ◽  
Robot Hands ◽  
Biomimetic Robot

Due to the dexterous manipulation capability and low metabolic energy consumption property of the human hand, many robotic hands were designed and manufactured that are inspired from the human hand. One of the technical challenges in designing biomimetic robot hands is the control scheme. The control algorithm used in a robot hand is expected to ensure the tracking of reference trajectories of fingertips and joint angles with high accuracy, reliability, and smoothness. In this chapter, trajectory-tracking performances of different types of widely used control strategies (i.e. classical, robust, and intelligent controllers) are comparatively evaluated. To accomplish this evaluation, PID, sliding mode, and fuzzy logic controllers are implemented on a biomimetic robot hand finger model and simulation results are quantitatively analyzed. Pros and cons of the corresponding control algorithms are also discussed.

scholarly journals Design and Calibration of a Force/Tactile Sensor for Dexterous Manipulation

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 966 ◽  
Author(s):  
Marco Costanzo ◽  
Giuseppe De Maria ◽  
Ciro Natale ◽  
Salvatore Pirozzi
Keyword(s):  
Experimental Validation ◽  
Sensory System ◽  
Tactile Sensor ◽  
Contact Forces ◽  
Dexterous Manipulation ◽  
Soft Contact ◽  
Grasping Force ◽  
Sense Of Touch ◽  
Human Sense ◽  
Robotic Applications

This paper presents the design and calibration of a new force/tactile sensor for robotic applications. The sensor is suitably designed to provide the robotic grasping device with a sensory system mimicking the human sense of touch, namely, a device sensitive to contact forces, object slip and object geometry. This type of perception information is of paramount importance not only in dexterous manipulation but even in simple grasping tasks, especially when objects are fragile, such that only a minimum amount of grasping force can be applied to hold the object without damaging it. Moreover, sensing only forces and not moments can be very limiting to securely grasp an object when it is grasped far from its center of gravity. Therefore, the perception of torsional moments is a key requirement of the designed sensor. Furthermore, the sensor is also the mechanical interface between the gripper and the manipulated object, therefore its design should consider also the requirements for a correct holding of the object. The most relevant of such requirements is the necessity to hold a torsional moment, therefore a soft distributed contact is necessary. The presence of a soft contact poses a number of challenges in the calibration of the sensor, and that is another contribution of this work. Experimental validation is provided in real grasping tasks with two sensors mounted on an industrial gripper.

scholarly journals Cognitive Interaction Analysis in Human–Robot Collaboration Using an Assembly Task

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1317
Author(s):  
Alejandro Chacón ◽  
Pere Ponsa ◽  
Cecilio Angulo
Keyword(s):  
Interaction Analysis ◽  
Mental Workload ◽  
Cognitive Task ◽  
Physical Work ◽  
Work Skills ◽  
Dexterous Manipulation ◽  
Assembly Task ◽  
Cognitive Interaction ◽  
Human Operators ◽  
Assembly Tasks

In human–robot collaborative assembly tasks, it is necessary to properly balance skills to maximize productivity. Human operators can contribute with their abilities in dexterous manipulation, reasoning and problem solving, but a bounded workload (cognitive, physical, and timing) should be assigned for the task. Collaborative robots can provide accurate, quick and precise physical work skills, but they have constrained cognitive interaction capacity and low dexterous ability. In this work, an experimental setup is introduced in the form of a laboratory case study in which the task performance of the human–robot team and the mental workload of the humans are analyzed for an assembly task. We demonstrate that an operator working on a main high-demanding cognitive task can also comply with a secondary task (assembly) mainly developed for a robot asking for some cognitive and dexterous human capacities producing a very low impact on the primary task. In this form, skills are well balanced, and the operator is satisfied with the working conditions.

Soft magnetic skin for super-resolution tactile sensing with force self-decoupling

2021 ◽  
Vol 6 (51) ◽  
pp. eabc8801
Author(s):  
Youcan Yan ◽  
Zhe Hu ◽  
Zhengbao Yang ◽  
Wenzhen Yuan ◽  
Chaoyang Song ◽  
...  
Keyword(s):  
External Disturbance ◽  
Super Resolution ◽  
Tactile Sensor ◽  
Human Robot Interaction ◽  
Tactile Sensors ◽  
Dexterous Manipulation ◽  
Robot Interaction ◽  
Flexible Film ◽  
Challenging Tasks ◽  
External Forces

Human skin can sense subtle changes of both normal and shear forces (i.e., self-decoupled) and perceive stimuli with finer resolution than the average spacing between mechanoreceptors (i.e., super-resolved). By contrast, existing tactile sensors for robotic applications are inferior, lacking accurate force decoupling and proper spatial resolution at the same time. Here, we present a soft tactile sensor with self-decoupling and super-resolution abilities by designing a sinusoidally magnetized flexible film (with the thickness ~0.5 millimeters), whose deformation can be detected by a Hall sensor according to the change of magnetic flux densities under external forces. The sensor can accurately measure the normal force and the shear force (demonstrated in one dimension) with a single unit and achieve a 60-fold super-resolved accuracy enhanced by deep learning. By mounting our sensor at the fingertip of a robotic gripper, we show that robots can accomplish challenging tasks such as stably grasping fragile objects under external disturbance and threading a needle via teleoperation. This research provides new insight into tactile sensor design and could be beneficial to various applications in robotics field, such as adaptive grasping, dexterous manipulation, and human-robot interaction.

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