Fig 10 - uploaded by Wei Dong
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The measurement data and the edges identification result when the orientation of the robot system is fixed to 0.4 o ,-0.4 o and 0.4 o respectively. (Blue lines: identified edges based on experiments; red lines: identified edges based on parameters estimation)
Source publication
This paper introduces a novel atomic force microscope (AFM) and parallel robot integrated micro-force measurement system whose objective is the measurement of adhesion force between planar micro-objects. This paper is mainly focused on the kinematics parameters estimation between the objects to be measured, the parallel robot and the AFM system in...
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Citations
... However, in the case of AFM, the force sensor probe only provides an information about tip sample distance along one axis, which complicates the problem. This perceived lack of information has been assessed by the introduction of patterns or shapes in the AFM sample substrate in order to provide a reference for the probe [9] [10]. The authors in [10] have proposed a method in which a 6-DOF Parallel robotic platform was used as a sample holder and AFM scanner. ...
... This perceived lack of information has been assessed by the introduction of patterns or shapes in the AFM sample substrate in order to provide a reference for the probe [9] [10]. The authors in [10] have proposed a method in which a 6-DOF Parallel robotic platform was used as a sample holder and AFM scanner. In this case, the AFM probe was fixed at a set position and the 6-DOF platform performed the trajectory. ...
This article proposes a method for the correction of angular deviations caused during the fixing process of samples prepared for Atomic Force Microscopy (AFM). The correction is done using the angular control of a 6-DOF PPPS parallel platform were the sample is placed, while the AFM scan is performed by a 3-DOF serial cartesian robot with a tuning fork probe designed to perform FM-AFM. The method uses the generic x, y, and z data provided by the AFM after performing a scan on a free surface of the sample substrate. This is used to calculate the plane that closest approximates the points by solving a system of linear equations. This plane is then used to estimate the angular corrections that the 6-DOF parallel robot has to do in order to compensate the deviations. The proposed algorithm can be performed iteratively in order to refine the correction. The method also does not require any special preparation of the substrate. It only requires to have a free surface to scan. Experiments are performed using this algorithm to correct the orientation deviation of a substrate of V1 High-grade mica. The results show that the method is able to correct the angular deviation of the sample relatively to the AFM probe with an error of 0.2°after only two iterations of the algorithm.
... Les premiers travaux que nous 15 avons menés portent sur des premières mesures entre une surface plane 50µm×50µm et un plan infini montrant l'impact de l'angle de contact sur la mesure de pull-off [52]. ...
... Relevés expérimentaux illustrant la calibration partielle du robot de mesure de force Ces premiers résultats ont montré que l'utilisation d'un levier d'AFM comme d'un capteur de contact sembleêtre un moyen de mesure pertinent pour mesurer des postures d'un robot de précisionévoluant sur des faibles courses.Ces travaux ont effectués lors du séjour post-doctoral de Wei Dong que j'ai encadré avec le support de David Rostoucher[52]. ...
This manuscript deals with a synthesis of scienti c contributions in the led of robotic micromanipulation. The manipulation of microscopic objects, whose behavior is driven by surface e ects, requires new handling and assembly solutions. The general approach proposed in this document consist in manipulate and assemble microcomponents inside a liquid. Indeed, we have shown that the use of a liquid modify signi cantly the problematics of micromanipulation and induces some advantages. For example, trajectories of the object are stabilized because of viscosity or surface e ects can be controlled using physical chemistry principle.The scienti c analysis presented in chapter 1 is based on 3 steps used during the study of robotic micromanipulation in liquid medium whose bibliographic context is presented in chapter 2. The rst step is the modeling of the interaction forces between micro-objects in order to predict and simulate their behavior. Based on this model, micromanipulation strategies (chapter 4) have been proposed, and scienti c works have been focused on their characterization and their control. These micromanipulation strategies are a key point of the construction of micro-assembly platforms reported in chapter 5. The perspectives of these works (chapter 6) deals with the exploration of the no man's land between microscience et nanoscience, the proposition of methods for complex and/or functional microassemblies, and the robotic micromanipulation of biological cells.
This letter presents a method to determine and control the center of rotation of a parallel micro-robotic platform used as a sample holder for an Atomic Force Microscope (AFM). The AFM is operating inside a Scanning Electron Microscope (SEM) for correlative AFM in SEM imaging. The objective is to spatially co-localize the Pivot Point (PP) and the AFM tip at any region of interest of a sample within the reachable workspace of the AFM. To do so, SEM images are used to land the AFM tip on a desired Point Of Interest (POI). Topographic data obtained with the AFM are used to calculate the Tool Center Point (TCP) of the robot and to identify the coordinates of the POI in the AFM sample holder reference frame. The position of the PP is then controlled relying on the TCP and SEM vision to finally been able to perform, in a controlled way, in-plane rotations of the sample holder around the AFM tip with a micrometer precision. This work shows for the first time how SEM and AFM data can be used in tandem to calibrate the rotational degrees of freedom of an AFM system.
The “AX=XB” sensor calibration problem is ubiquitous in the fields of robotics and computer vision. In this problem A,X, and B are each homogeneous transformations (i.e., rigid-body motions) with A and B given from sensor measurements, and X is the unknown that is sought. For decades this problem is known to be solvable for X when a set of exactly measured compatible A’s and B’s with known correspondence is given. However, in practical problems, it is often the case that the data streams containing the A’s and B’s will present at different sample rates, they will be asynchronous, and each stream may contain gaps in information. Practical scenarios in which this can happen include hand-eye calibration and ultrasound image registration. We therefore present a method for calculating the calibration transformation, X, that works for data without any a priori knowledge of the correspondence between the As and Bs.
