Integration of visual and joint information to enable linear reaching motions

A new dynamics-driven control law was developed for a robot arm, based on the feedback control law which uses the linear transformation directly from work space to joint space. This was validated using a simulation of a two-joint planar robot arm and an optimisation algorithm was used to find the optimum matrix to generate straight trajectories of the end-effector in the work space. We found that this linear matrix can be decomposed into the rotation matrix representing the orientation of the goal direction and the joint relation matrix (MJRM) representing the joint response to errors in the Cartesian work space. The decomposition of the linear matrix indicates the separation of path planning in terms of the direction of the reaching motion and the synergies of joint coordination. Once the MJRM is numerically obtained, the feedfoward planning of reaching direction allows us to provide asymptotically stable, linear trajectories in the entire work space through rotational transformation, completely avoiding the use of inverse kinematics. Our dynamics-driven control law suggests an interesting framework for interpreting human reaching motion control alternative to the dominant inverse method based explanations, avoiding expensive computation of the inverse kinematics and the point-to-point control along the desired trajectories

First measurement of hadronic event shapes in pp collisions at √s = 7 TeV

This is the Pre-Print version of the Article - Copyright @ 2011 ElsevierHadronic event shapes have been measured in proton-proton collisions at sqrt(s)=7 TeV, with a data sample collected with the CMS detector at the LHC. The sample corresponds to an integrated luminosity of 3.2 inverse picobarns. Event-shape distributions, corrected for detector response, are compared with five models of QCD multijet production

A Simulation Tool for Kinematics Analysis of a Serial Robot

2nd International Conference on Design, Simulation, Manufacturing - The Innovation Exchange (DSMIE) -- JUN 11-14, 2019 -- Lutsk, UKRAINEWOS: 000515081800059Robot programming is a very significant task in the field of robotics. Off-line programming (OLP) is a method performed before robot manipulation. It is the manual editing of the robot code using computer software to simulate the real robotic scenarios. Task sequence planning, short-term production, flexibility during operation and expecting real behaviour of the robots are some of the reasons that make the users prefer OLP. Operations can be visualized in many processes such as welding, cutting, even medical applications. In this study, off-line models are offered including the forward and inverse kinematics of a six Degree-Of-Freedom (DOF) serial robot manipulator (Denso VP-6242G). Robotic Toolbox combined with GUI Development Environment in Matlab (R) is used for the forward kinematics solution. A Matlab (R) Simulink model with Simmechanics blocks is used in the inverse kinematic analysis. Visualization is enriched by 3D Solidworks (R) models of the robot parts. Basic motion examples that can be used in many areas are presented.Sumy State Univ, Lutsk Natl Tech Univ, Int Assoc Technol Dev & Innova

Cognitive Modeling for Automating Learning in Visually-guided Manipulative Tasks

International audienceRobot manipulators, as general-purpose machines, can be used to perform various tasks. Though, adaptations to specific scenarios require of some technical efforts. In particular, the descriptions of the task result in a robot program which must be modified whenever changes are introduced. Another source of variations are undesired changes due to the entropic properties of systems; in effect, robots must be re-calibrated with certain frequency to produce the desired results. To ensure adaptability , cognitive robotists aim to design systems capable of learning and decision making. Moreover, control techniques such as visual-servoing allow robust control under inaccuracies in the estimates of the system's parameters. This paper reports the design of a platform called CRR, which combines the computational cognition paradigm for decision making and learning, with the visual-servoing control technique for the automation of manipulative tasks

Behavior Acquisition via Vision-Based Robot Learning

We introduce our approach that makes a robot learn to behave adequately to accomplish a given task at hand through the interactions with its environment with less a priori knowledge about the environment or the robot itself. We briey present three research topics of vision-based robot learning in each of which visual perception is tightly coupled with actuator eects so as to learn an adequate behavior. First, a method of vision-based reinforcement learning by which a robot learns to shoot a ball into a goal is presented. Next, \motion sketch" for a one-eyed mobile robot to learn several behaviors such as obstacle avoidance and target pursuit is introduced. Finally, we show a method of purposive visual control consisting of an on-line estimator and a feedback/feedforward controller for uncalibrated camera-manipulator systems. All topics include the real robot experiments. 1 Introduction Realization of autonomous agents that organize their own internal structure in order to take actio..