873 research outputs found
Neuro-mechanical entrainment in a bipedal robotic walking platform
In this study, we investigated the use of van der Pol oscillators in a 4-dof embodied bipedal robotic platform for the purposes of planar walking. The oscillator controlled the hip and knee joints of the robot and was capable of generating waveforms with the correct frequency and phase so as to entrain with the mechanical system. Lowering its oscillation frequency resulted in an increase to the walking pace, indicating exploitation of the global natural dynamics. This is verified by its operation in absence of entrainment, where faster limb motion results in a slower overall walking pace
Neuro-mechanical entrainment in a bipedal robotic walking platform
In this study, we investigated the use of van der Pol oscillators in a 4-dof embodied bipedal robotic platform for the purposes of planar walking. The oscillator controlled the hip and knee joints of the robot and was capable of generating waveforms with the correct frequency and phase so as to entrain with the mechanical system. Lowering its oscillation frequency resulted in an increase to the walking pace, indicating exploitation of the global natural dynamics. This is verified by its operation in absence of entrainment, where faster limb motion results in a slower overall walking pace
Recursive mass matrix factorization and inversion: An operator approach to open- and closed-chain multibody dynamics
This report advances a linear operator approach for analyzing the dynamics of systems of joint-connected rigid bodies.It is established that the mass matrix M for such a system can be factored as M=(I+H phi L)D(I+H phi L) sup T. This yields an immediate inversion M sup -1=(I-H psi L) sup T D sup -1 (I-H psi L), where H and phi are given by known link geometric parameters, and L, psi and D are obtained recursively by a spatial discrete-step Kalman filter and by the corresponding Riccati equation associated with this filter. The factors (I+H phi L) and (I-H psi L) are lower triangular matrices which are inverses of each other, and D is a diagonal matrix. This factorization and inversion of the mass matrix leads to recursive algortihms for forward dynamics based on spatially recursive filtering and smoothing. The primary motivation for advancing the operator approach is to provide a better means to formulate, analyze and understand spatial recursions in multibody dynamics. This is achieved because the linear operator notation allows manipulation of the equations of motion using a very high-level analytical framework (a spatial operator algebra) that is easy to understand and use. Detailed lower-level recursive algorithms can readily be obtained for inspection from the expressions involving spatial operators. The report consists of two main sections. In Part 1, the problem of serial chain manipulators is analyzed and solved. Extensions to a closed-chain system formed by multiple manipulators moving a common task object are contained in Part 2. To retain ease of exposition in the report, only these two types of multibody systems are considered. However, the same methods can be easily applied to arbitrary multibody systems formed by a collection of joint-connected regid bodies
Full-Body Torque-Level Non-linear Model Predictive Control for Aerial Manipulation
Non-linear model predictive control (nMPC) is a powerful approach to control
complex robots (such as humanoids, quadrupeds, or unmanned aerial manipulators
(UAMs)) as it brings important advantages over other existing techniques. The
full-body dynamics, along with the prediction capability of the optimal control
problem (OCP) solved at the core of the controller, allows to actuate the robot
in line with its dynamics. This fact enhances the robot capabilities and
allows, e.g., to perform intricate maneuvers at high dynamics while optimizing
the amount of energy used. Despite the many similarities between humanoids or
quadrupeds and UAMs, full-body torque-level nMPC has rarely been applied to
UAMs.
This paper provides a thorough description of how to use such techniques in
the field of aerial manipulation. We give a detailed explanation of the
different parts involved in the OCP, from the UAM dynamical model to the
residuals in the cost function. We develop and compare three different nMPC
controllers: Weighted MPC, Rail MPC, and Carrot MPC, which differ on the
structure of their OCPs and on how these are updated at every time step. To
validate the proposed framework, we present a wide variety of simulated case
studies. First, we evaluate the trajectory generation problem, i.e., optimal
control problems solved offline, involving different kinds of motions (e.g.,
aggressive maneuvers or contact locomotion) for different types of UAMs. Then,
we assess the performance of the three nMPC controllers, i.e., closed-loop
controllers solved online, through a variety of realistic simulations. For the
benefit of the community, we have made available the source code related to
this work.Comment: Submitted to Transactions on Robotics. 17 pages, 16 figure
Geometry-aware Manipulability Learning, Tracking and Transfer
Body posture influences human and robots performance in manipulation tasks,
as appropriate poses facilitate motion or force exertion along different axes.
In robotics, manipulability ellipsoids arise as a powerful descriptor to
analyze, control and design the robot dexterity as a function of the
articulatory joint configuration. This descriptor can be designed according to
different task requirements, such as tracking a desired position or apply a
specific force. In this context, this paper presents a novel
\emph{manipulability transfer} framework, a method that allows robots to learn
and reproduce manipulability ellipsoids from expert demonstrations. The
proposed learning scheme is built on a tensor-based formulation of a Gaussian
mixture model that takes into account that manipulability ellipsoids lie on the
manifold of symmetric positive definite matrices. Learning is coupled with a
geometry-aware tracking controller allowing robots to follow a desired profile
of manipulability ellipsoids. Extensive evaluations in simulation with
redundant manipulators, a robotic hand and humanoids agents, as well as an
experiment with two real dual-arm systems validate the feasibility of the
approach.Comment: Accepted for publication in the Intl. Journal of Robotics Research
(IJRR). Website: https://sites.google.com/view/manipulability. Code:
https://github.com/NoemieJaquier/Manipulability. 24 pages, 20 figures, 3
tables, 4 appendice
Manipulation Planning for Forceful Human-Robot-Collaboration
This thesis addresses the problem of manipulation planning for forceful human-robot collaboration. Particularly, the focus is on the scenario where a human applies a sequence of changing external forces through forceful operations (e.g. cutting a circular piece off a board) on an object that is grasped by a cooperative robot. We present a range of planners that 1) enable the robot to stabilize and position the object under the human applied forces by exploiting supports from both the object-robot and object-environment contacts; 2) improve task efficiency by minimizing the need of configuration and grasp changes required by the changing external forces; 3) improve human comfort during the forceful interaction by optimizing the defined comfort criteria.
We first focus on the instance of using only robotic grasps, where the robot is supposed to grasp/regrasp the object multiple times to keep it stable under the changing external forces. We introduce a planner that can generate an efficient manipulation plan by intelligently deciding when the robot should change its grasp on the object as the human applies the forces, and choosing subsequent grasps such that they minimize the number of regrasps required in the long-term. The planner searches for such an efficient plan by first finding a minimal sequence of grasp configurations that are able to keep the object stable under the changing forces, and then generating connecting trajectories to switch between the planned configurations, i.e. planning regrasps. We perform the search for such a grasp (configuration) sequence by sampling stable configurations for the external forces, building an operation graph using these stable configurations and then searching the operation graph to minimize the number of regrasps. We solve the problem of bimanual regrasp planning under the assumption of no support surface, enabling the robot to regrasp an object in the air by finding intermediate configurations at which both the bimanual and unimanual grasps can hold the object stable under gravity. We present a variety of experiments to show the performance of our planner, particularly in minimizing the number of regrasps for forceful manipulation tasks and planning stable regrasps.
We then explore the problem of using both the object-environment contacts and object-robot contacts, which enlarges the set of stable configurations and thus boosts the robot’s capability in stabilizing the object under external forces. We present a planner that can intelligently exploit the environment’s and robot’s stabilization capabilities within a unified planning framework to search for a minimal number of stable contact configurations. A big computational bottleneck in this planner is due to the static stability analysis of a large number of candidate configurations. We introduce a containment relation between different contact configurations, to efficiently prune the stability checking process. We present a set of real-robot and simulated experiments illustrating the effectiveness of the proposed framework. We present a detailed analysis of the proposed containment relationship, particularly in improving the planning efficiency.
We present a planning algorithm to further improve the cooperative robot behaviour concerning human comfort during the forceful human-robot interaction. Particularly, we are interested in empowering the robot with the capability of grasping and positioning the object not only to ensure the object stability against the human applied forces, but also to improve human experience and comfort during the interaction. We address human comfort as the muscular activation level required to apply a desired external force, together with the human spatial perception, i.e. the so-called peripersonal-space comfort during the interaction. We propose to maximize both comfort metrics to optimize the robot and object configuration such that the human can apply a forceful operation comfortably. We present a set of human-robot drilling and cutting experiments which verify the efficiency of the proposed metrics in improving the overall comfort and HRI experience, without compromising the force stability.
In addition to the above planning work, we present a conic formulation to approximate the distribution of a forceful operation in the wrench space with a polyhedral cone, which enables the planner to efficiently assess the stability of a system configuration even in the presence of force uncertainties that are inherent in the human applied forceful operations. We also develop a graphical user interface, which human users can easily use to specify various forceful tasks, i.e. sequences of forceful operations on selected objects, in an interactive manner. The user interface ties in human task specification, on-demand manipulation planning and robot-assisted fabrication together. We present a set of human-robot experiments using the interface demonstrating the feasibility of our system.
In short, in this thesis we present a series of planners for object manipulation under changing external forces. We show the object contacts with the robot and the environment enable the robot to manipulate an object under external forces, while making the most of the object contacts has the potential to eliminate redundant changes during manipulation, e.g. regrasp, and thus improve task efficiency and smoothness. We also show the necessity of optimizing human comfort in planning for forceful human-robot manipulation tasks. We believe the work presented here can be a key component in a human-robot collaboration framework
Inertial Parameter Identification Including Friction and Motor Dynamics
Identification of inertial parameters is fundamental for the implementation
of torque-based control in humanoids. At the same time, good models of friction
and actuator dynamics are critical for the low-level control of joint torques.
We propose a novel method to identify inertial, friction and motor parameters
in a single procedure. The identification exploits the measurements of the PWM
of the DC motors and a 6-axis force/torque sensor mounted inside the kinematic
chain. The partial least-square (PLS) method is used to perform the regression.
We identified the inertial, friction and motor parameters of the right arm of
the iCub humanoid robot. We verified that the identified model can accurately
predict the force/torque sensor measurements and the motor voltages. Moreover,
we compared the identified parameters against the CAD parameters, in the
prediction of the force/torque sensor measurements. Finally, we showed that the
estimated model can effectively detect external contacts, comparing it against
a tactile-based contact detection. The presented approach offers some
advantages with respect to other state-of-the-art methods, because of its
completeness (i.e. it identifies inertial, friction and motor parameters) and
simplicity (only one data collection, with no particular requirements).Comment: Pre-print of paper presented at Humanoid Robots, 13th IEEE-RAS
International Conference on, Atlanta, Georgia, 201
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
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