This paper presents a functionality that has been developed for the home-made AFM-in-SEM robotic system at the ISIR laboratory. The method allows extending the range of an Atomic Force Microscope (AFM) and dealing with drift issues by fusing multiple individually AFM topography patches. The merging of the patches into a single image is done through a Generalized Procrustes Analysis Iterative Closest Point (GPA-ICP) algorithm. To validate the effectiveness of the approach, an AFM image of a TGX1 calibration grid and a 3.4billion-year-old organic-walled microfossil are reconstructed by automatically merging 50 AFM elementary topography patches of dimension 0.9 µm × 1.2 µm based on feature matching. The overlap between two adjacent patches is 50 % and 33 % in the X and Y axes respectively. The result is a coherent 3.2 µm × 3.0 µm drift-free long range AFM topography without significant artifacts. The method is tested using an AFM-in-SEM system based on a 3-DOF cartesian robot equipped with inertial piezoelectric actuators. This method can be used to extend the range of any type of AFM with a dual XY stage setup. Thus, it opens the door for high-resolution long-range AFM by adding a long-range coarse resolution stage to a preexisting AFM system all without needing to actuate both stages simultaneously.

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.

The spherical joint is an effective solution to design parallel micro-robotic systems with rotation capabilities in the three-dimensional space. This type of joint has however some non-linear characteristics, such as the clearance, which affect the positioning accuracy in micro-robotic tasks. The starting point of this study lies in experimental observations of rotation errors from a 3-PPPS 6-DOF parallel micro-robotic systems operating inside a scanning electron microscope. The objective of the paper is to assess the role of the spherical joints in the rotation errors and to evaluate whether the joints non-linearities can cause errors with the same order of magnitude as those observed experimentally. To this end, the first part of the study addresses the modeling of 3-PPPS 6-DOF parallel micro-robotic systems with spherical joints including the clearance. This model allows for analysing the effect of the clearance on position and rotation accuracies of the micro-robotic system. It is found by simulations that the same positioning behavior as in the experiments occurs when the clearance of the spherical joint is included in the model, supporting the hypothesis. Therefore, it is concluded that clearance in spherical joints has a significant effect on the precision of parallel type micro-robotic systems which opens new challenges in the control of poly-articulated micro-robotic systems with clearance compensation.

Active force sensors are key instruments to get around the tradeoff between the sensitivity and the measurement range of conventional passive force sensors. Thanks to their quasi-infinite stiffness in closed loop, active sensors can be applied for force measurements on samples with a wide range of stiffness without interference with the mechanical parameters of the sensor. MEMS (Micro-Electro Mechanical Systems) active force sensors have been wildly developed in the literature but they are ill adapted for force measurements at the Newton level needed in meso-scale robotics. In this article, a novel structure for a meso-scale active force sensor is proposed for the measurement of forces from the milli-newton to the newton.This novel meso-scale sensor is based on a nil-stiffness guidance and an electromagnetic actuation. This paper deals with its design, identification, calibration and closed loop control. The sensor exhibits nil-stiffness characteristic in open loop and an almost infinite stiffness in closed loop. This allows measuring forces with a large range of gradients. First experiments shows the ability of this new sensor architecture to measure low frequency forces up to 0.8N with a precision of 0.03 N and a closed loop -20 dB cutoff frequency of 73.9Hz.

Collaborative robotics allows merging the best capabilities of humans and robots to perform complex tasks. This allows the user to interact with remote and directly inaccessible environments such as the micro-scale world. This interaction is made possible by the bidirectional exchange of information (displacement - force) between the user and the environment through a haptic interface. The effectiveness of the human/robot interaction is highly dependent on how the human feels the forces. This is a key point to enable humans to make the right decisions in a collaborative task. This paper discusses the design of a dynamic observer to estimate the forces applied by a human operator on a class of parallel pantograph-type haptic interfaces used to control small-scale robotic systems. The objective is to reject disturbances in order to improve the human force perception capability over a wide frequency range. A dynamic pantograph model is proposed and experimentally validated. The observer is designed on the basis of the proposed dynamic model and its efficiency in estimating the applied human force is demonstrated for the first time with pantograph-type interfaces. Experimental validation first shows the effectiveness of the perturbation observer for external human force estimation with a response time of less than 0.2 s and a mean error of less than 7 mN and then the effectiveness of the controller in improving the quality of human sensation of forces down to 10 mN.

Scanning Electron Microscopy (SEM) is an ideal observation tool for small scales robotics. It has the potential to achieve automated nano-robotic tasks such as nano-handling and nano-assembly. Path following control of nano-robot end effectors using SEM vision feedback is a key for an intuitive programming of elementary robotic tasks sequences. It requires the ability to track end effectors under various SEM scan speeds. SEM suffers however from tricky issues that limits robotic tracking capabilities. This paper focuses on one specific issue related to the compromise between the scan speed and the image quality. This restriction seriously limits the performance of conventional vision tracking algorithms when used with electron images. At high scan speed, the image quality is very noisy making very difficult to differentiate the robot end effector from the background, hence limiting the tracking capabilities. The work related in this paper explores for the first time the potential value of Convolutional Neural Networks (ConvNet) in the context of nano-robotic vision tracking inside SEM. The aim is to localize an end-effector, AFM cantilever in the case of the study, from SEM images for any scan speed configuration and despite of low images quality. For that purpose, a data set of AFM tip images is build up from SEM images for the learning algorithm. Network performances are estimated under different SEM scan speeds. Thanks to the learning algorithm, experimental results show robust AFM tip tracking capabilities inside the SEM under various scan speed conditions.

Nano-robotic systems based on Piezoelectric StickSlip (PSS) actuators have become increasingly popular in research and industry for semi-automated and automated tasks at small scales. For an efficient use of PSS actuators, a series of research have been fulfilled on design process, dynamic modeling, driving methods and position control. However, there have been very few investigations on velocity control of PSS actuators. Velocity control is important to enable the nano-robotic system to generate a smooth and efficient motion and to avoid the undesired inertial shock of the end effector. This paper deals with velocity characterization and control strategies for nano-robotic systems based on PSS actuators. The range of achievable velocities on PSS actuators is studied in air and vacuum environments. This analysis allows the definition of a detailed map of the velocity characteristics in forward and backward directions of motion. Velocity control strategies are then studied based on an instantaneous velocity feedback and an average velocity feedback. Results of the proposed method show the first experimental demonstration of velocity control for PSS actuators in medium and high speed configurations opening new perspectives on the use of nano-robotic systems in dynamic automated tasks.

In order to define trajectory-tracking strategies for nano-robotic systems using piezoelectric stick-slip actuators, the dynamics of the elementary actuator must be studied and well modeled. The modeling of this class of actuators is complex because several nonlinear parameters are involved. In this paper, we propose a systematic modeling methodology of piezoelectric stick-slip actuators for nano-robotic systems control. The main idea is the proposition of an augmented voltage/frequency rate dependent modeling of the friction force based on a multi-state elasto-plastic formulation. Experimental and simulation results demonstrate the efficiency of the model in the time and the frequency domains. As a case of study, the proposed model is used to define a control strategy in order to detect collisions when the nano-robotic system is operating inside a Scanning Electron Microscope (SEM). This application demonstrates the need of a voltage/frequency rate dependent modeling.

This paper addresses modeling issues for a class of nano-robotic systems using piezoelectric stick-slip actuators. The work focuses on the friction force modeling to describe the dynamics of a stick-slip actuator in a wide operating range needed in nano-robotics. Based on the theory of the single state elasto-plastic model and on an experimental analysis, necessary conditions on presiding modeling are highlighted. The conditions allow describing the dynamics of stick-slip type actuators for both scanning mode and stepping mode in the time and the frequency domains and for backward and forward directions of the motion. The proposed dynamic model opens new perspective for closed loop control of nano-robotic system.

This paper presents a study about the effects of environmental noise on millimeter sized grippers in cantilever configuration. The study is motivated and conducted aiming at assessing the level of accuracy loss when performing micromanipulation/microassembly tasks in noisy environments as well in typical microrobotics laboratories as in industrial locations or operating rooms. Ground motion and acoustic noises within a typical microrobotic laboratory are characterized in the frequency domain and their effects on cantilevers of different lengths are inspected. The relevance of a typical vibration isolation table is evaluated and the effects of low and high acoustic noises are assessed. A modeling of a cantilever with base excitation is thereafter conducted in the state space using finite difference formulation and a stabilization of a disturbed cantilever is obtained at the nanometer level in noisy environments allowing perspectives to high precision micromanipulation tasks in hostile locations.

MEMS-based microgrippers with integrated force sensor have proved their efficiency to perform dexterous micromanipulation tasks through gripping forces sensing and control. For force control, knowledge based models are more relevant and gives better physical significance than the use of black box models. However this approach is often limited by many problems commonly encountered in the MEMS (micro electromechanical systems) structures such as: complex architectures, nonlinear behaviors and parameters uncertainties due to fabrication process at the micrometer scale. For these reasons theoretical approaches must be compared with experiments. This paper describes a modelling approach of a MEMS-based microgripper with integrated force sensor while handling micro-glass balls of 80μm diameter. Therefore, a state space representation is developed to couple both the dynamics of the actuation and sensing subsystems of the gripper through the stiffness of the manipulated object. A knowledge based model is obtained for small displacements at the tip of the gripper arms (small gripping forces) and is compared with experimental approaches. Good agreements are observed allowing interesting perspectives for the control.