98 research outputs found
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
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
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
An anthropomorphic design for a minimally invasive surgical system based on a survey of surgical technologies, techniques and training
© 2013 John Wiley & Sons, Ltd. Background: Over the past century, abdominal surgery has seen a rapid transition from open procedures to less invasive methods, such as robot-assisted minimally invasive surgery (MIS). This study aimed to investigate and discuss the needs of MIS in terms of instrumentation and to inform the design of a novel instrument. Methods: A survey was conducted among surgeons regarding their opinions on surgical training, surgical systems, how satisfied they were with them and how easy they were to use. A concept for MIS robotic instrumentation was then developed and a series of focus groups with surgeons were run to discuss it. The initial prototype of the robotic instruments, herein demonstrated, comprises modular rigid links with soft joints actuated by shape memory alloy helix actuators; these instruments are controlled using a sensory hand exoskeleton. Results: The results of the survey, as well as those of the focus groups, are presented here. A first prototype of the system was built and initial laboratory tests have been conducted in order to evaluate this approach. Conclusions: The analysed data from both the survey and the focus groups justify the chosen concept of an anthropomorphic MIS robotic system which imitates the natural motion of the hands
Anthropomorphic Twisted String-Actuated Soft Robotic Gripper with Tendon-Based Stiffening
Realizing high-performance soft robotic grippers is challenging because of
the inherent limitations of the soft actuators and artificial muscles that
drive them, including low force output, small actuation range, and poor
compactness. Despite advances in this area, realizing compact soft grippers
with high dexterity and force output is still challenging. This paper explores
twisted string actuators (TSAs) to drive a soft robotic gripper. TSAs have been
used in numerous robotic applications, but their inclusion in soft robots has
been limited. The proposed design of the gripper was inspired by the human
hand. Tunable stiffness was implemented in the fingers with antagonistic TSAs.
The fingers' bending angles, actuation speed, blocked force output, and
stiffness tuning were experimentally characterized. The gripper achieved a
score of 6 on the Kapandji test and recreated 31 of the 33 grasps of the Feix
GRASP taxonomy. It exhibited a maximum grasping force of 72 N, which was almost
13 times its own weight. A comparison study revealed that the proposed gripper
exhibited equivalent or superior performance compared to other similar soft
grippers.Comment: 19 pages, 15 figure
Two-fingered Hand with Gear-type Synchronization Mechanism with Magnet for Improved Small and Offset Objects Grasping: F2 Hand
A problem that plagues robotic grasping is the misalignment of the object and
gripper due to difficulties in precise localization, actuation, etc.
Under-actuated robotic hands with compliant mechanisms are used to adapt and
compensate for these inaccuracies. However, these mechanisms come at the cost
of controllability and coordination. For instance, adaptive functions that let
the fingers of a two-fingered gripper adapt independently may affect the
coordination necessary for grasping small objects. In this work, we develop a
two-fingered robotic hand capable of grasping objects that are offset from the
gripper's center, while still having the requisite coordination for grasping
small objects via a novel gear-type synchronization mechanism with a magnet.
This gear synchronization mechanism allows the adaptive finger's tips to be
aligned enabling it to grasp objects as small as toothpicks and washers. The
magnetic component allows this coordination to automatically turn off when
needed, allowing for the grasping of objects that are offset/misaligned from
the gripper. This equips the hand with the capability of grasping light,
fragile objects (strawberries, creampuffs, etc) to heavy frying pan lids, all
while maintaining their position and posture which is vital in numerous
applications that require precise positioning or careful manipulation.Comment: 8 pages. Accepted at IEEE IROS 2023. An accompanying video is
available at https://www.youtube.com/watch?v=RAO7Qb2ZGN
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
Controling interactions in motion control systems
Design of motion control systems should take into account (a) unconstrained
motion performed without interaction with environment or other systems, (b) constrained motion performed by certain functional interaction with environment or other system. Control in both cases can be formulated in terms of maintaining desired system configuration what makes essentially the same structure for common tasks: trajectory tracking, interaction force control, compliance control etc. It will be shown that the same design approach can be used for systems that maintain some functional relations like parallel robots
Anthropomorphic surgical system for soft tissue robot-assisted surgery
Over the past century, abdominal surgery has seen a rapid transition from open procedures to less invasive methods such as laparoscopy and robot-assisted minimally invasive surgery (R-A MIS). These procedures have significantly decreased blood loss, postoperative morbidity and length of hospital stay in comparison with open surgery. R-A MIS has offered refined accuracy and more ergonomic instruments for surgeons, further minimising trauma to the patient.This thesis aims to investigate, design and prototype a novel system for R-A MIS that will provide more natural and intuitive manipulation of soft tissues and, at the same time, increase the surgeon's dexterity. The thesis reviews related work on surgical systems and discusses the requirements for designing surgical instrumentation. From the background research conducted in this thesis, it is clear that training surgeons in MIS procedures is becoming increasingly long and arduous. Furthermore, most available systems adopt a design similar to conventional laparoscopic instruments or focus on different techniques with debatable benefits. The system proposed in this thesis not only aims to reduce the training time for surgeons but also to improve the ergonomics of the procedure.In order to achieve this, a survey was conducted among surgeons, regarding their opinions on surgical training, surgical systems, how satisfied they are with them and how easy they are to use. A concept for MIS robotic instrumentation was then developed and a series of focus group meetings with surgeons were run to discuss it. The proposed system, named microAngelo, is an anthropomorphic master-slave system that comprises a three-digit miniature hand that can be controlled using the master, a three-digit sensory exoskeleton. While multi-fingered robotic hands have been developed for decades, none have been used for surgical operations. As the system has a human centred design, its relation to the human hand is discussed. Prototypes of both the master and the slave have been developed and their design and mechanisms is demonstrated. The accuracy and repeatability of the master as well as the accuracy and force capabilities of the slave are tested and discussed
SMC based bilateral control
Design of a motion control system should take into account (a) unconstrained motion performed without interaction with environment or other system, and
(b) constrained motion with system in contact with environment or another system or has certain functional interaction with another system. Control in both cases can be formulated in terms of maintaining desired system configuration what makes essentially the same structure for common tasks: trajectory tracking, interaction force control, compliance control etc. It will be shown that the same design approach can be used for systems that maintain some functional relation – like bilateral or multilateral systems, relation among mobile robots or control of haptic systems.
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