• Aucun résultat trouvé

2D robotic control of a planar dielectrophoresis-based system.

N/A
N/A
Protected

Academic year: 2021

Partager "2D robotic control of a planar dielectrophoresis-based system."

Copied!
7
0
0

Texte intégral

Figure

Fig. 1. Movement transmission used in robotics: (i) standard joints used in a majority of robots; (ii) compliant joints based on mechanical deformation used in high precision positionning systems; (iii) the third alternative:
Fig. 2. The electric charge density computed on the electrodes by applying the following electric voltages: U = [75V, 0,75V ].
Fig. 4. A dynamic modeling and DMS are used to compute the micro- micro-bead’s 3D trajectory.
Fig. 5. Geometry of the electrodes and applied voltages: definition of control parameters u x and u y .
+3

Références

Documents relatifs

Our model is composed of three parts: A structural model allowing the temporal representation of both topology and geometry; an event model that aims at detecting

Theoretical studies, like Monte-Carlo simulations, high temperature developments and many experiments lead to a strong evidence for a nonzero transition temperature

We will study the performance of different master equation for the description of the non-Markovian evolution of a qubit coupled to a spin bath [97], compare Markovian and

However, after a few images, the algorithm is no longer able to track accurately the object shape. The failure is mainly due to the fact that a 2D affine motion model cannot

In regard to time-op- timal control, we address the issue of existence and uniqueness of optimal trajectories, and explicitly compute the optimal control and the corresponding

Once the cameras are localized, the Structure-from-Motion reconstruction obtained from image correspondences is refined by means of a constrained nonlinear optimization that

Unité de recherche INRIA Rennes, Irisa, Campus universitaire de Beaulieu, 35042 RENNES Cedex Unité de recherche INRIA Rhône-Alpes, 655, avenue de l’Europe, 38330 MONTBONNOT ST

The feature’s descriptor was composed for each image cluster by: the 3D image processing data, representing the percentage of free space and obstacles, and the 2D image processing