3,276 research outputs found
Human-robot interaction for assistive robotics
This dissertation presents an in-depth study of human-robot interaction (HRI) withapplication to assistive robotics. In various studies, dexterous in-hand manipulation is included, assistive robots for Sit-To-stand (STS) assistance along with the human intention estimation. In Chapter 1, the background and issues of HRI are explicitly discussed. In Chapter 2, the literature review introduces the recent state-of-the-art research on HRI, such as physical Human-Robot Interaction (HRI), robot STS assistance, dexterous in hand manipulation and human intention estimation. In Chapter 3, various models and control algorithms are described in detail. Chapter 4 introduces the research equipment. Chapter 5 presents innovative theories and implementations of HRI in assistive robotics, including a general methodology of robotic assistance from the human perspective, novel hardware design, robotic sit-to-stand (STS) assistance, human intention estimation, and control
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
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
User Needs, Benefits, and Integration of Robotic Systems in a Space Station Laboratory
The methodology, results and conclusions of all tasks of the User Needs, Benefits, and Integration Study (UNBIS) of Robotic Systems in a Space Station Laboratory are summarized. Study goals included the determination of user requirements for robotics within the Space Station, United States Laboratory. In Task 1, three experiments were selected to determine user needs and to allow detailed investigation of microgravity requirements. In Task 2, a NASTRAN analysis of Space Station response to robotic disturbances, and acceleration measurement of a standard industrial robot (Intelledex Model 660) resulted in selection of two ranges of microgravity manipulation: Level 1 (10-3 to 10-5 G at greater than 1 Hz) and Level 2 (less than equal 10-6 G at 0.1 Hz). This task included an evaluation of microstepping methods for controlling stepper motors and concluded that an industrial robot actuator can perform milli-G motion without modification. Relative merits of end-effectors and manipulators were studied in Task 3 in order to determine their ability to perform a range of tasks related to the three microgravity experiments. An Effectivity Rating was established for evaluating these robotic system capabilities. Preliminary interface requirements for an orbital flight demonstration were determined in Task 4. Task 5 assessed the impact of robotics
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
Dynamic task allocation for a man-machine symbiotic system
This report presents a methodological approach to the dynamic allocation of tasks in a man-machine symbiotic system in the context of dexterous manipulation and teleoperation. This report addresses a symbiotic system containing two symbiotic partners which work toward controlling a single manipulator arm for the execution of a series of sequential manipulation tasks. It is proposed that an automated task allocator use knowledge about the constraints/criteria of the problem, the available resources, the tasks to be performed, and the environment to dynamically allocate task recommendations for the man and the machine. The presentation of the methodology includes discussions concerning the interaction of the knowledge areas, the flow of control, the necessary communication links, and the replanning of the task allocation. Examples of task allocation are presented to illustrate the results of this methodolgy
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