3,053 research outputs found
Kinematics and Workspace Analysis of a Three-Axis Parallel Manipulator: the Orthoglide
The paper addresses kinematic and geometrical aspects of the Orthoglide, a
three-DOF parallel mechanism. This machine consists of three fixed linear
joints, which are mounted orthogonally, three identical legs and a mobile
platform, which moves in the Cartesian x-y-z space with fixed orientation. New
solutions to solve inverse/direct kinematics are proposed and we perform a
detailed workspace and singularity analysis, taking into account specific joint
limit constraints
Design method for an anthropomorphic hand able to gesture and grasp
This paper presents a numerical method to conceive and design the kinematic
model of an anthropomorphic robotic hand used for gesturing and grasping. In
literature, there are few numerical methods for the finger placement of
human-inspired robotic hands. In particular, there are no numerical methods,
for the thumb placement, that aim to improve the hand dexterity and grasping
capabilities by keeping the hand design close to the human one. While existing
models are usually the result of successive parameter adjustments, the proposed
method determines the fingers placements by mean of empirical tests. Moreover,
a surgery test and the workspace analysis of the whole hand are used to find
the best thumb position and orientation according to the hand kinematics and
structure. The result is validated through simulation where it is checked that
the hand looks well balanced and that it meets our constraints and needs. The
presented method provides a numerical tool which allows the easy computation of
finger and thumb geometries and base placements for a human-like dexterous
robotic hand.Comment: IEEE International Conference on Robotics and Automation, May 2015,
Seattle, United States. IEEE, 2015, Proceeding IEEE International Conference
on Robotics and Automatio
Kinematic calibration of Orthoglide-type mechanisms from observation of parallel leg motions
The paper proposes a new calibration method for parallel manipulators that
allows efficient identification of the joint offsets using observations of the
manipulator leg parallelism with respect to the base surface. The method
employs a simple and low-cost measuring system, which evaluates deviation of
the leg location during motions that are assumed to preserve the leg
parallelism for the nominal values of the manipulator parameters. Using the
measured deviations, the developed algorithm estimates the joint offsets that
are treated as the most essential parameters to be identified. The validity of
the proposed calibration method and efficiency of the developed numerical
algorithms are confirmed by experimental results. The sensitivity of the
measurement methods and the calibration accuracy are also studied
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
IK-FA, a new heuristic inverse kinematics solver using firefly algorithm
In this paper, a heuristic method based on Firefly Algorithm is proposed for inverse kinematics problems in articulated robotics. The proposal is called, IK-FA. Solving inverse kinematics, IK, consists in finding a set of joint-positions allowing a specific point of the system to achieve a target position. In IK-FA, the Fireflies positions are assumed to be a possible solution for joints elementary motions. For a robotic system with a known forward kinematic model, IK-Fireflies, is used to generate iteratively a set of joint motions, then the forward kinematic model of the system is used to compute the relative Cartesian positions of a specific end-segment, and to compare it to the needed target position. This is a heuristic approach for solving inverse kinematics without computing the inverse model. IK-FA tends to minimize the distance to a target position, the fitness function could be established as the distance between the obtained forward positions and the desired one, it is subject to minimization. In this paper IK-FA is tested over a 3 links articulated planar system, the evaluation is based on statistical analysis of the convergence and the solution quality for 100 tests. The impact of key FA parameters is also investigated with a focus on the impact of the number of fireflies, the impact of the maximum iteration number and also the impact of (a, Ăź, Âż, d) parameters. For a given set of valuable parameters, the heuristic converges to a static fitness value within a fix maximum number of iterations. IK-FA has a fair convergence time, for the tested configuration, the average was about 2.3394 Ă— 10-3 seconds with a position error fitness around 3.116 Ă— 10-8 for 100 tests. The algorithm showed also evidence of robustness over the target position, since for all conducted tests with a random target position IK-FA achieved a solution with a position error lower or equal to 5.4722 Ă— 10-9.Peer ReviewedPostprint (author's final draft
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