1,085 research outputs found
Innovative robot hand designs of reduced complexity for dexterous manipulation
This thesis investigates the mechanical design of robot hands to sensibly reduce the system complexity in terms of the number of actuators and sensors, and control needs for performing grasping and in-hand manipulations of unknown objects.
Human hands are known to be the most complex, versatile, dexterous manipulators in nature, from being able to operate sophisticated surgery to carry out a wide variety of daily activity tasks (e.g. preparing food, changing cloths, playing instruments, to name some). However, the understanding of why human hands can perform such fascinating tasks still eludes complete comprehension.
Since at least the end of the sixteenth century, scientists and engineers have tried to match the sensory and motor functions of the human hand. As a result, many contemporary humanoid and anthropomorphic robot hands have been developed to closely replicate the appearance and dexterity of human hands, in many cases using sophisticated designs that integrate multiple sensors and actuators---which make them prone to error and difficult to operate and control, particularly under uncertainty.
In recent years, several simplification approaches and solutions have been proposed to develop more effective and reliable dexterous robot hands. These techniques, which have been based on using underactuated mechanical designs, kinematic synergies, or compliant materials, to name some, have opened up new ways to integrate hardware enhancements to facilitate grasping and dexterous manipulation control and improve reliability and robustness.
Following this line of thought, this thesis studies four robot hand hardware aspects for enhancing grasping and manipulation, with a particular focus on dexterous in-hand manipulation. Namely: i) the use of passive soft fingertips; ii) the use of rigid and soft active surfaces in robot fingers; iii) the use of robot hand topologies to create particular in-hand manipulation trajectories; and iv) the decoupling of grasping and in-hand manipulation by introducing a reconfigurable palm.
In summary, the findings from this thesis provide important notions for understanding the significance of mechanical and hardware elements in the performance and control of human manipulation. These findings show great potential in developing robust, easily programmable, and economically viable robot hands capable of performing dexterous manipulations under uncertainty, while exhibiting a valuable subset of functions of the human hand.Open Acces
Systematic object-invariant in-hand manipulation via reconfigurable underactuatuation: introducing the RUTH gripper
We introduce a reconfigurable underactuated robot hand able to perform systematic prehensile in-hand manipulations regardless of object size or shape. The hand utilises a two-degree-of-freedom five-bar linkage as the palm of the gripper, with three three-phalanx underactuated fingers—jointly controlled by a single actuator—connected to the mobile revolute joints of the palm. Three actuators are used in the robot hand system in total, one for controlling the force exerted on objects by the fingers through an underactuated tendon system, and two for changing the configuration of the palm and thus the positioning of the fingers. This novel layout allows decoupling grasping and manipulation, facilitating the planning and execution of in-hand manipulation operations. The reconfigurable palm provides the hand with a large grasping versatility, and allows easy computation of a map between task space and joint space for manipulation based on distance-based linkage kinematics. The motion of objects of different sizes and shapes from one pose to another is then straightforward and systematic, provided the objects are kept grasped.This is guaranteed independently and passively by the underactuated fingers using a custom tendon routing method, which allows no tendon length variation when the relative finger base positions change with palm reconfigurations. We analyse the theoretical grasping workspace and grasping and manipulation capability of the hand, present algorithms forcomputing the manipulation map and in-hand manipulation planning, and evaluate all these experimentally. Numericaland empirical results of several manipulation trajectories with objects of different size and shape clearly demonstrate the viability of the proposed concept
On Neuromechanical Approaches for the Study of Biological Grasp and Manipulation
Biological and robotic grasp and manipulation are undeniably similar at the
level of mechanical task performance. However, their underlying fundamental
biological vs. engineering mechanisms are, by definition, dramatically
different and can even be antithetical. Even our approach to each is
diametrically opposite: inductive science for the study of biological systems
vs. engineering synthesis for the design and construction of robotic systems.
The past 20 years have seen several conceptual advances in both fields and the
quest to unify them. Chief among them is the reluctant recognition that their
underlying fundamental mechanisms may actually share limited common ground,
while exhibiting many fundamental differences. This recognition is particularly
liberating because it allows us to resolve and move beyond multiple paradoxes
and contradictions that arose from the initial reasonable assumption of a large
common ground. Here, we begin by introducing the perspective of neuromechanics,
which emphasizes that real-world behavior emerges from the intimate
interactions among the physical structure of the system, the mechanical
requirements of a task, the feasible neural control actions to produce it, and
the ability of the neuromuscular system to adapt through interactions with the
environment. This allows us to articulate a succinct overview of a few salient
conceptual paradoxes and contradictions regarding under-determined vs.
over-determined mechanics, under- vs. over-actuated control, prescribed vs.
emergent function, learning vs. implementation vs. adaptation, prescriptive vs.
descriptive synergies, and optimal vs. habitual performance. We conclude by
presenting open questions and suggesting directions for future research. We
hope this frank assessment of the state-of-the-art will encourage and guide
these communities to continue to interact and make progress in these important
areas
The KIT swiss knife gripper for disassembly tasks: a multi-functional gripper for bimanual manipulation with a single arm
© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This work presents the concept of a robotic gripper designed for the disassembly of electromechanical devices that comprises several innovative ideas. Novel concepts include the ability to interchange built-in tools without the need to grasp them, the ability to reposition grasped objects in-hand, the capability of performing classic dual arm manipulation within the gripper and the utilization of classic industrial robotic arms kinematics within a robotic gripper. We analyze state of the art grippers and robotic hands designed for dexterous in-hand manipulation and extract common characteristics and weak points. The presented concept is obtained from the task requirements for disassembly of electromechanical devices and it is then evaluated for general purpose grasping, in-hand manipulation and operations with tools. We further present the CAD design for a first prototype.Peer ReviewedPostprint (author's final draft
Learning by Demonstration and Robust Control of Dexterous In-Hand Robotic Manipulation Skills
Dexterous robotic manipulation of unknown objects can open the way to novel tasks and applications of robots in semi-structured and unstructured settings, from advanced industrial manufacturing to exploration of harsh environments. However, it is challenging for at least three reasons: the desired motion of the object might be too complex to be described analytically, precise models of the manipulated objects are not available, the controller should simultaneously ensure both a robust grasp and an effective in-hand motion. To solve these issues we propose to learn in-hand robotic manipulation tasks from human demonstrations, using Dynamical Movement Primitives (DMPs), and to reproduce them with a robust compliant controller based on the Virtual Springs Framework (VSF), that employs real-time feedback of the contact forces measured on the robot fingertips. With this solution, the generalization capabilities of DMPs can be transferred successfully to the dexterous in-hand manipulation problem: we demonstrate this by presenting real-world experiments of in-hand translation and rotation of unknown objects
Contact mechanics for soft robotic fingers: modeling and experimentation
Human fingers possess mechanical characteristics, which enable them to manipulate objects. In robotics, the study of soft fingertip materials for manipulation has been going on for a while; however, almost all previous researches have been carried on hemispherical shapes whereas this study concentrates on the use of hemicylindrical shapes. These shapes were found to be more resistant to elastic deformations for the same materials. The purpose of this work is to generate a modified nonlinear contact-mechanics theory for modeling soft fingertips, which is proposed as a power-law equation. The contact area of a hemicylindrical soft fingertip is proportional to the normal force raised to the power of Îłcy, which ranges from 0 to 1/2. Subsuming the Timoshenko and Goodier (S. P. Timoshenko and J. N. Goodier, Theory of Elasticity, 3rd ed. (McGraw-Hill, New York, 1970) pp. 414-420) linear contact theory for cylinders confirms the proposed power equation. We applied a weighted least-squares curve fitting to analyze the experimental data for different types of silicone (RTV 23, RTV 1701, and RTV 240). Our experimental results supported the proposed theoretical prediction. Results for human fingers and hemispherical soft fingers were also compare
All the Feels: A dexterous hand with large area sensing
High cost and lack of reliability has precluded the widespread adoption of
dexterous hands in robotics. Furthermore, the lack of a viable tactile sensor
capable of sensing over the entire area of the hand impedes the rich, low-level
feedback that would improve learning of dexterous manipulation skills. This
paper introduces an inexpensive, modular, robust, and scalable platform - the
DManus- aimed at resolving these challenges while satisfying the large-scale
data collection capabilities demanded by deep robot learning paradigms. Studies
on human manipulation point to the criticality of low-level tactile feedback in
performing everyday dexterous tasks. The DManus comes with ReSkin sensing on
the entire surface of the palm as well as the fingertips. We demonstrate
effectiveness of the fully integrated system in a tactile aware task - bin
picking and sorting. Code, documentation, design files, detailed assembly
instructions, trained models, task videos, and all supplementary materials
required to recreate the setup can be found on
http://roboticsbenchmarks.org/platforms/dmanusComment: 6 pages + references and appendix, 7 figures. Submitted to ICRA 202
A hierarchical sensorimotor control framework for human-in-the-loop robotic hands.
Human manual dexterity relies critically on touch. Robotic and prosthetic hands are much less dexterous and make little use of the many tactile sensors available. We propose a framework modeled on the hierarchical sensorimotor controllers of the nervous system to link sensing to action in human-in-the-loop, haptically enabled, artificial hands
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