267 research outputs found
Towards Developing Gripper to obtain Dexterous Manipulation
Artificial hands or grippers are essential elements in many robotic systems, such as, humanoid,
industry, social robot, space robot, mobile robot, surgery and so on. As humans, we use
our hands in different ways and can perform various maneuvers such as writing, altering
posture of an object in-hand without having difficulties. Most of our daily activities are
dependent on the prehensile and non-prehensile capabilities of our hand. Therefore, the
human hand is the central motivation of grasping and manipulation, and has been explicitly
studied from many perspectives such as, from the design of complex actuation, synergy, use
of soft material, sensors, etc; however to obtain the adaptability to a plurality of objects along
with the capabilities of in-hand manipulation of our hand in a grasping device is not easy,
and not fully evaluated by any developed gripper.
Industrial researchers primarily use rigid materials and heavy actuators in the design for
repeatability, reliability to meet dexterity, precision, time requirements where the required
flexibility to manipulate object in-hand is typically absent. On the other hand, anthropomorphic
hands are generally developed by soft materials. However they are not deployed
for manipulation mainly due to the presence of numerous sensors and consequent control
complexity of under-actuated mechanisms that significantly reduce speed and time requirements
of industrial demand. Hence, developing artificial hands or grippers with prehensile
capabilities and dexterity similar to human like hands is challenging, and it urges combined
contributions from multiple disciplines such as, kinematics, dynamics, control, machine
learning and so on. Therefore, capabilities of artificial hands in general have been constrained
to some specific tasks according to their target applications, such as grasping (in biomimetic
hands) or speed/precision in a pick and place (in industrial grippers).
Robotic grippers developed during last decades are mostly aimed to solve grasping
complexities of several objects as their primary objective. However, due to the increasing
demands of industries, many issues are rising and remain unsolved such as in-hand manipulation
and placing object with appropriate posture. Operations like twisting, altering
orientation of object within-hand, require significant dexterity of the gripper that must be
achieved from a compact mechanical design at the first place. Along with manipulation,
speed is also required in many robotic applications. Therefore, for the available speed and
design simplicity, nonprehensile or dynamic manipulation is widely exploited. The nonprehensile
approach however, does not focus on stable grasping in general. Also, nonprehensile
or dynamic manipulation often exceeds robot\u2019s kinematic workspace, which additionally
urges installation of high speed feedback and robust control. Hence, these approaches are
inapplicable especially when, the requirements are grasp oriented such as, precise posture
change of a payload in-hand, placing payload afterward according to a strict final configuration.
Also, addressing critical payload such as egg, contacts (between gripper and egg)
cannot be broken completely during manipulation. Moreover, theoretical analysis, such as
contact kinematics, grasp stability cannot predict the nonholonomic behaviors, and therefore,
uncertainties are always present to restrict a maneuver, even though the gripper is capable of
doing the task.
From a technical point of view, in-hand manipulation or within-hand dexterity of a gripper
significantly isolates grasping and manipulation skills from the dependencies on contact type,
a priory knowledge of object model, configurations such as initial or final postures and also
additional environmental constraints like disturbance, that may causes breaking of contacts
between object and finger. Hence, the property (in-hand manipulation) is important for a
gripper in order to obtain human hand skill.
In this research, these problems (to obtain speed, flexibility to a plurality of grasps,
within-hand dexterity in a single gripper) have been tackled in a novel way. A gripper
platform named Dexclar (DEXterous reConfigurable moduLAR) has been developed in order
to study in-hand manipulation, and a generic spherical payload has been considered at the
first place. Dexclar is mechanism-centric and it exploits modularity and reconfigurability to
the aim of achieving within-hand dexterity rather than utilizing soft materials. And hence,
precision, speed are also achievable from the platform. The platform can perform several
grasps (pinching, form closure, force closure) and address a very important issue of releasing
payload with final posture/ configuration after manipulation. By exploiting 16 degrees of
freedom (DoF), Dexclar is capable to provide 6 DoF motions to a generic spherical or
ellipsoidal payload. And since a mechanism is reliable, repeatable once it has been properly
synthesized, precision and speed are also obtainable from them. Hence Dexclar is an ideal
starting point to study within-hand dexterity from kinematic point of view.
As the final aim is to develop specific grippers (having the above capabilities) by exploiting
Dexclar, a highly dexterous but simply constructed reconfigurable platform named
VARO-fi (VARiable Orientable fingers with translation) is proposed, which can be used as
an industrial end-effector, as well as an alternative of bio-inspired gripper in many robotic
applications. The robust four fingered VARO-fi addresses grasp, in-hand manipulation and
release (payload with desired configuration) of plurality of payloads, as demonstrated in this
thesis.
Last but not the least, several tools and end-effectors have been constructed to study
prehensile and non-prehensile manipulation, thanks to Bayer Robotic challenge 2017, where
the feasibility and their potentiality to use them in an industrial environment have been
validated.
The above mentioned research will enhance a new dimension for designing grippers
with the properties of dexterity and flexibility at the same time, without explicit theoretical
analysis, algorithms, as those are difficult to implement and sometime not feasible for real
system
The role of morphology of the thumb in anthropomorphic grasping : a review
The unique musculoskeletal structure of the human hand brings in wider dexterous capabilities to grasp and manipulate a repertoire of objects than the non-human primates. It has been widely accepted that the orientation and the position of the thumb plays an important role in this characteristic behavior. There have been numerous attempts to develop anthropomorphic robotic hands with varying levels of success. Nevertheless, manipulation ability in those hands is to be ameliorated even though they can grasp objects successfully. An appropriate model of the thumb is important to manipulate the objects against the fingers and to maintain the stability. Modeling these complex interactions about the mechanical axes of the joints and how to incorporate these joints in robotic thumbs is a challenging task. This article presents a review of the biomechanics of the human thumb and the robotic thumb designs to identify opportunities for future anthropomorphic robotic hands
Ground Robotic Hand Applications for the Space Program study (GRASP)
This document reports on a NASA-STDP effort to address research interests of the NASA Kennedy Space Center (KSC) through a study entitled, Ground Robotic-Hand Applications for the Space Program (GRASP). The primary objective of the GRASP study was to identify beneficial applications of specialized end-effectors and robotic hand devices for automating any ground operations which are performed at the Kennedy Space Center. Thus, operations for expendable vehicles, the Space Shuttle and its components, and all payloads were included in the study. Typical benefits of automating operations, or augmenting human operators performing physical tasks, include: reduced costs; enhanced safety and reliability; and reduced processing turnaround time
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
A Robust Controller for Stable 3D Pinching using Tactile Sensing
This paper proposes a controller for stable grasping of unknown-shaped
objects by two robotic fingers with tactile fingertips. The grasp is stabilised
by rolling the fingertips on the contact surface and applying a desired
grasping force to reach an equilibrium state. The validation is both in
simulation and on a fully-actuated robot hand (the Shadow Modular Grasper)
fitted with custom-built optical tactile sensors (based on the BRL TacTip). The
controller requires the orientations of the contact surfaces, which are
estimated by regressing a deep convolutional neural network over the tactile
images. Overall, the grasp system is demonstrated to achieve stable equilibrium
poses on various objects ranging in shape and softness, with the system being
robust to perturbations and measurement errors. This approach also has promise
to extend beyond grasping to stable in-hand object manipulation with multiple
fingers.Comment: 8 pages, 10 figures, 1 appendix. Accepted for publication in IEEE
Robotics and Automation Letters and in IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS 2021). Supplemental video:
https://youtu.be/rfQesw3FDA
Kinematic Analysis of Multi-Fingered, Anthropomorphic Robotic Hands
The ability of stable grasping and fine manipulation with the multi-fingered robot hand with required precision and dexterity is playing an increasingly important role in the applications like service robots, rehabilitation, humanoid robots, entertainment robots, industries etc.. A number of multi-fingered robotic hands have been developed by various researchers in the past. The distinct advantages of a multi-fingered robot hand having structural similarity with human hand motivate the need for an anthropomorphic robot hand. Such a hand provides a promising base for supplanting human hand in execution of tedious, complicated and dangerous tasks, especially in situations such as manufacturing, space, undersea etc. These can also be used in orthopaedic rehabilitation of humans for improving the quality of the life of people having orthopedically and neurological disabilities. The developments so far are mostly driven by the application requirements. There are a number of bottlenecks with industrial grippers as regards to the stability of grasping objects of irregular geometries or complex manipulation operations. A multi-fingered robot hand can be made to mimic the movements of a human hand. The present piece of research work attempts to conceptualize and design a multi-fingered, anthropomorphic robot hand by structurally imitating the human hand.
In the beginning, a brief idea about the history, types of robotic hands and application of multi-fingered hands in various fields are presented. A review of literature based on different aspects of the multi-fingered hand like structure, control, optimization, gasping etc. is made. Some of the important and more relevant literatures are elaborately discussed and a brief analysis is made on the outcomes and shortfalls with respect to multi-fingered hands. Based on the analysis of the review of literature, the research work aims at developing an improved anthropomorphic robot hand model in which apart from the four fingers and a thumb, the palm arch effect of human hand is also considered to increase its dexterity.
A robotic hand with five anthropomorphic fingers including the thumb and palm arch effect having 25 degrees-of-freedom in all is investigated in the present work. Each individual finger is considered as an open loop kinematic chain and each finger segment is considered as a link of the manipulator. The wrist of the hand is considered as a fixed point.
The kinematic analyses of the model for both forward kinematics and inverse kinematic are carried out. The trajectories of the tip positions of the thumb and the fingers with respect to local coordinate system are determined and plotted. This gives the extreme position of the fingertips which is obtained from the forward kinematic solution with the help of MATLAB. Similarly, varying all the joint
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angles of the thumb and fingers in their respective ranges, the reachable workspace of the hand model is obtained. Adaptive Neuro-Fuzzy Inference System (ANFIS) is used for solving the inverse kinematic problem of the fingers.
Since the multi-fingered hand grasps the object mainly through its fingertips and the manipulation of the object is facilitated by the fingers due to their dexterity, the grasp is considered to be force-closure grasp. The grasping theory and different types of contacts between the fingertip and object are presented and the conditions for stable and equilibrium grasp are elaborately discussed. The proposed hand model is simulated to grasp five different shaped objects with equal base dimension and height. The forces applied on the fingertip during grasping are calculated. The hand model is also analysed using ANSYS to evaluate the stresses being developed at various points in the thumb and fingers. This analysis was made for the hand considering two different hand materials i.e. aluminium alloy and structural steel.
The solution obtained from the forward kinematic analysis of the hand determines the maximum size for differently shaped objects while the solution to the inverse kinematic problem indicates the configurations of the thumb and the fingers inside the workspace of the hand. The solutions are predicted in which all joint angles are within their respective ranges.
The results of the stress analysis of the hand model show that the structure of the fingers and the hand as a whole is capable of handling the selected objects.
The robot hand under investigation can be realized and can be a very useful tool for many critical areas such as fine manipulation of objects, combating orthopaedic or neurological impediments, service robotics, entertainment robotics etc.
The dissertation concludes with a summary of the contribution and the scope of further work
An integrated dexterous robotic testbed for space applications
An integrated dexterous robotic system was developed as a testbed to evaluate various robotics technologies for advanced space applications. The system configuration consisted of a Utah/MIT Dexterous Hand, a PUMA 562 arm, a stereo vision system, and a multiprocessing computer control system. In addition to these major subsystems, a proximity sensing system was integrated with the Utah/MIT Hand to provide capability for non-contact sensing of a nearby object. A high-speed fiber-optic link was used to transmit digitized proximity sensor signals back to the multiprocessing control system. The hardware system was designed to satisfy the requirements for both teleoperated and autonomous operations. The software system was designed to exploit parallel processing capability, pursue functional modularity, incorporate artificial intelligence for robot control, allow high-level symbolic robot commands, maximize reusable code, minimize compilation requirements, and provide an interactive application development and debugging environment for the end users. An overview is presented of the system hardware and software configurations, and implementation is discussed of subsystem functions
Learning to Use Chopsticks in Diverse Gripping Styles
Learning dexterous manipulation skills is a long-standing challenge in
computer graphics and robotics, especially when the task involves complex and
delicate interactions between the hands, tools and objects. In this paper, we
focus on chopsticks-based object relocation tasks, which are common yet
demanding. The key to successful chopsticks skills is steady gripping of the
sticks that also supports delicate maneuvers. We automatically discover
physically valid chopsticks holding poses by Bayesian Optimization (BO) and
Deep Reinforcement Learning (DRL), which works for multiple gripping styles and
hand morphologies without the need of example data. Given as input the
discovered gripping poses and desired objects to be moved, we build
physics-based hand controllers to accomplish relocation tasks in two stages.
First, kinematic trajectories are synthesized for the chopsticks and hand in a
motion planning stage. The key components of our motion planner include a
grasping model to select suitable chopsticks configurations for grasping the
object, and a trajectory optimization module to generate collision-free
chopsticks trajectories. Then we train physics-based hand controllers through
DRL again to track the desired kinematic trajectories produced by the motion
planner. We demonstrate the capabilities of our framework by relocating objects
of various shapes and sizes, in diverse gripping styles and holding positions
for multiple hand morphologies. Our system achieves faster learning speed and
better control robustness, when compared to vanilla systems that attempt to
learn chopstick-based skills without a gripping pose optimization module and/or
without a kinematic motion planner
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