31 research outputs found
Planning hand-arm grasping motions with human-like appearance
© 2018 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 worksFinalista de l’IROS Best Application Paper Award a la 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, ICROS.This paper addresses the problem of obtaining human-like motions on hand-arm robotic systems performing pick-and-place actions. The focus is set on the coordinated movements of the robotic arm and the anthropomorphic mechanical hand, with which the arm is equipped. For this, human movements performing different grasps are captured and mapped to the robot in order to compute the human hand synergies. These synergies are used to reduce the complexity of the planning phase by reducing the dimension of the search space. In addition, the paper proposes a sampling-based planner, which guides the motion planning ollowing the synergies. The introduced approach is tested in an application example and thoroughly compared with other state-of-the-art planning algorithms, obtaining better results.Peer ReviewedAward-winningPostprint (author's final draft
Modeling human-likeness in approaching motions of dual-arm autonomous robots
© 2018 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 addresses the problem of obtaining human-like motions with an anthropomorphic dual-arm torso assembled on a mobile platform. The focus is set on the coordinated movements of the robotic arms and the robot base while approaching a table to subsequently perform a bimanual manipulation task. For this, human movements are captured and mapped to the robot in order to compute the human dual-arm synergies. Since the demonstrated synergies change depending on the robot position, a recursive Cartesian-space discretization is presented based on these differences. Thereby, different movements of the arms are assigned to different regions of the Cartesian space. As an application example, a motion-planning algorithm exploiting this information is proposed and used.Postprint (published version
Planning grasping motions for humanoid robots
This paper addresses the problem of obtaining the required motions for a humanoid robot to perform grasp actions trying to mimic the coordinated hand–arm movements humans do. The first step is the data acquisition and analysis, which consists in capturing human movements while grasping several everyday objects (covering four possible grasp types), mapping them to the robot and computing the hand motion synergies for the pre-grasp and grasp phases (per grasp type). Then, the grasp and motion synthesis step is done, which consists in generating potential grasps for a given object using the four family types, and planning the motions using a bi-directional multi-goal sampling-based planner, which efficiently guides the motion planning following the synergies in a reduced search space, resulting in paths with human-like appearance. The approach has been tested in simulation, thoroughly compared with other state-of-the-art planning algorithms obtaining better results, and also implemented in a real robot.Peer ReviewedPostprint (author's final draft
A Novel Uncalibrated Visual Servoing Controller Baesd on Model-Free Adaptive Control Method with Neural Network
Nowadays, with the continuous expansion of application scenarios of robotic
arms, there are more and more scenarios where nonspecialist come into contact
with robotic arms. However, in terms of robotic arm visual servoing,
traditional Position-based Visual Servoing (PBVS) requires a lot of calibration
work, which is challenging for the nonspecialist to cope with. To cope with
this situation, Uncalibrated Image-Based Visual Servoing (UIBVS) frees people
from tedious calibration work. This work applied a model-free adaptive control
(MFAC) method which means that the parameters of controller are updated in real
time, bringing better ability of suppression changes of system and environment.
An artificial intelligent neural network is applied in designs of controller
and estimator for hand-eye relationship. The neural network is updated with the
knowledge of the system input and output information in MFAC method. Inspired
by "predictive model" and "receding-horizon" in Model Predictive Control (MPC)
method and introducing similar structures into our algorithm, we realizes the
uncalibrated visual servoing for both stationary targets and moving
trajectories. Simulated experiments with a robotic manipulator will be carried
out to validate the proposed algorithm.Comment: 16 pages, 8 figure
Learning Constrained Distributions of Robot Configurations with Generative Adversarial Network
In high dimensional robotic system, the manifold of the valid configuration
space often has a complex shape, especially under constraints such as
end-effector orientation or static stability. We propose a generative
adversarial network approach to learn the distribution of valid robot
configurations under such constraints. It can generate configurations that are
close to the constraint manifold. We present two applications of this method.
First, by learning the conditional distribution with respect to the desired
end-effector position, we can do fast inverse kinematics even for very high
degrees of freedom (DoF) systems. Then, we use it to generate samples in
sampling-based constrained motion planning algorithms to reduce the necessary
projection steps, speeding up the computation. We validate the approach in
simulation using the 7-DoF Panda manipulator and the 28-DoF humanoid robot
Talos