9,546 research outputs found
A hyper-redundant manipulator
“Hyper-redundant” manipulators have a very large number of actuatable degrees of freedom. The benefits of hyper-redundant robots include the ability to avoid obstacles, increased robustness with respect to mechanical failure, and the ability to perform new forms of robot locomotion and grasping. The authors examine hyper-redundant manipulator design criteria and the physical implementation of one particular design: a variable geometry truss
Asymmetric Dual-Arm Task Execution using an Extended Relative Jacobian
Coordinated dual-arm manipulation tasks can be broadly characterized as
possessing absolute and relative motion components. Relative motion tasks, in
particular, are inherently redundant in the way they can be distributed between
end-effectors. In this work, we analyse cooperative manipulation in terms of
the asymmetric resolution of relative motion tasks. We discuss how existing
approaches enable the asymmetric execution of a relative motion task, and show
how an asymmetric relative motion space can be defined. We leverage this result
to propose an extended relative Jacobian to model the cooperative system, which
allows a user to set a concrete degree of asymmetry in the task execution. This
is achieved without the need for prescribing an absolute motion target.
Instead, the absolute motion remains available as a functional redundancy to
the system. We illustrate the properties of our proposed Jacobian through
numerical simulations of a novel differential Inverse Kinematics algorithm.Comment: Accepted for presentation at ISRR19. 16 Page
Grasping bulky objects with two anthropomorphic hands
© 2016 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 worksThis paper presents an algorithm to compute precision grasps for bulky objects using two anthropomorphic hands. We use objects modeled as point clouds obtained from a sensor camera or from a CAD model. We then process the point clouds dividing them into two set of slices where we look for sets of triplets of points. Each triplet must accomplish some physical conditions based on the structure of the hands. Then, the triplets of points from each set of slices are evaluated to find a combination that satisfies the force closure condition (FC). Once one valid couple of triplets have been found the inverse kinematics of the system is computed in order to know if the corresponding points are reachable by the hands, if so, motion planning and a collision check are performed to asses if the final grasp configuration of the system is suitable. The paper
inclu des some application examples of the proposed approachAccepted versio
Goal Set Inverse Optimal Control and Iterative Re-planning for Predicting Human Reaching Motions in Shared Workspaces
To enable safe and efficient human-robot collaboration in shared workspaces
it is important for the robot to predict how a human will move when performing
a task. While predicting human motion for tasks not known a priori is very
challenging, we argue that single-arm reaching motions for known tasks in
collaborative settings (which are especially relevant for manufacturing) are
indeed predictable. Two hypotheses underlie our approach for predicting such
motions: First, that the trajectory the human performs is optimal with respect
to an unknown cost function, and second, that human adaptation to their
partner's motion can be captured well through iterative re-planning with the
above cost function. The key to our approach is thus to learn a cost function
which "explains" the motion of the human. To do this, we gather example
trajectories from pairs of participants performing a collaborative assembly
task using motion capture. We then use Inverse Optimal Control to learn a cost
function from these trajectories. Finally, we predict reaching motions from the
human's current configuration to a task-space goal region by iteratively
re-planning a trajectory using the learned cost function. Our planning
algorithm is based on the trajectory optimizer STOMP, it plans for a 23 DoF
human kinematic model and accounts for the presence of a moving collaborator
and obstacles in the environment. Our results suggest that in most cases, our
method outperforms baseline methods when predicting motions. We also show that
our method outperforms baselines for predicting human motion when a human and a
robot share the workspace.Comment: 12 pages, Accepted for publication IEEE Transaction on Robotics 201
Reuleaux: Robot Base Placement by Reachability Analysis
Before beginning any robot task, users must position the robot's base, a task
that now depends entirely on user intuition. While slight perturbation is
tolerable for robots with moveable bases, correcting the problem is imperative
for fixed-base robots if some essential task sections are out of reach. For
mobile manipulation robots, it is necessary to decide on a specific base
position before beginning manipulation tasks.
This paper presents Reuleaux, an open source library for robot reachability
analyses and base placement. It reduces the amount of extra repositioning and
removes the manual work of identifying potential base locations. Based on the
reachability map, base placement locations of a whole robot or only the arm can
be efficiently determined. This can be applied to both statically mounted
robots, where position of the robot and work piece ensure the maximum amount of
work performed, and to mobile robots, where the maximum amount of workable area
can be reached. Solutions are not limited only to vertically constrained
placement, since complicated robotics tasks require the base to be placed at
unique poses based on task demand.
All Reuleaux library methods were tested on different robots of different
specifications and evaluated for tasks in simulation and real world
environment. Evaluation results indicate that Reuleaux had significantly
improved performance than prior existing methods in terms of time-efficiency
and range of applicability.Comment: Submitted to International Conference of Robotic Computing 201
Learning Task Priorities from Demonstrations
Bimanual operations in humanoids offer the possibility to carry out more than
one manipulation task at the same time, which in turn introduces the problem of
task prioritization. We address this problem from a learning from demonstration
perspective, by extending the Task-Parameterized Gaussian Mixture Model
(TP-GMM) to Jacobian and null space structures. The proposed approach is tested
on bimanual skills but can be applied in any scenario where the prioritization
between potentially conflicting tasks needs to be learned. We evaluate the
proposed framework in: two different tasks with humanoids requiring the
learning of priorities and a loco-manipulation scenario, showing that the
approach can be exploited to learn the prioritization of multiple tasks in
parallel.Comment: Accepted for publication at the IEEE Transactions on Robotic
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
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