A micromanipulation setup for comparative tests of microgrippers

A micromanipulation setup allowing comparative tests of manipulation micro tools has been developed. Repeatability measurements of positioning as well as optimization of manipulation conditions can be run with parts of typically 5 to 50μm over a large set of parameters including environment conditions, substrate and tip specifications, and different strategies (robot trajectories at picking and releasing time). The workstation consists of a high precise parallel robot, the Delta3, to position the gripper, linear stages to place the parts in the field of view and two microscopes for the visual feedback and position measurement. The setup is placed in a chamber for controlling relative humidity and temperature. An interface was developed to integrate every kind of tool on the robot. Automated operations and measurement have been carried out based on localization and tracking of micro objects and gripper. Integration of micro tools was successfully accomplished and comparative tests were executed with micro tweezers. Sub micrometer position repeatability was achieved with a success rate of pick and pick operations of 95%

Micro-gripper à haute dynamique

Dans le monde microscopique, la force de gravité devient négligeable par rapport aux forces d’adhésion (capillarité, Van der Waals). Ce projet vise à utiliser ces forces pour la prise de bille d’une taille caractéristique de 50[μm]. La dépose quand à elle s’effectue de manière dynamique, en utilisant l’inertie de la bille soumise à une forte accélération. Le but de ce projet est de caractériser la prise (taux de succès) et la dépose (seuil d’accélération, taux de succès, précision et répétabilité). La prise dépend essentiellement du type de matériaux utilisés ; sa caractérisation a été faite pour un gripper en silicium et un gripper en verre dans l’air ambiant (20% humidité relative) et l’azote (3%). Pour fournir une accélération, un piézoélectrique est utilisé (cristal qui se déforme lorsqu’une tension est appliquée à ses bornes). L’avantage principal est que la déformation est bien contrôlable en intensité et direction. La dépose quand à elle a été caractérisée dans les mêmes conditions que la prise et en excitant le piézoélectrique avec des sinus continus ou en créant une seule impulsion Au final, il a été montré que cette technique est viable pour la micromanipulation. Toutefois des points clés nécessaires à un bon contrôle des opérations ont été identifiés, notamment au niveau de la rigidité du substrat pour éviter l’écrasement des objets et au niveau de la qualité des surfaces de contact

Changeur d'outils pour station automatisée de micromanipulation

Une station de micromanipulation de haute précision, basée sur le robot Delta3 (3ddl, courses de 4mm), a été développée au LSRO. Elle permet des opérations automatiques de "pick and place", ainsi que la caractérisation des outils de manipulation dans des conditions spécifiques (environnement, matériaux, stratégies). Les outils utilisent différents principes de prise et de dépose. Un support standard a donc été développé et permet de les interfacer avec le robot. Dans le but d’une automatisation complète de ces opérations, il est nécessaire d’intégrer un changeur d’outils sur cette station. Un autre atout majeur est qu’il sera alors possible de tester différents outils dans des conditions identiques au niveau de l’environnement. Pour ce faire, une zone de stockage des outils devra être aménagée à l’intérieur de la boîte et le changement devra être possible sans ouverture de l’enceinte

Characterization of an inertial micro gripper based on adhesion forces

Adhesive forces become predominant in the micro world comparing to the gravity effect implying the development of new micro manipulation strategies. This paper presents the design and conception of a gripper that use the inertial principle for the release (applying a high acceleration, in the order of 10’000g) and the adhesion for catching a micro part of 50μm with the goal of precisely control the position after release. Experiments were conducted and showed a positioning repeatability of 2μm to 6μm depending on the relative humidity with a success rate of more than 90%

In situ micro gripper shaping by electro discharge machining

A machining process based on Electro Discharge Machining (EDM) has been developed and allows producing flexible and low-cost tweezers-like grippers or grippers shaped for a specific application. It can be easily included in a micromanipulation setup and requires no additional hardware except the sparks generator: the machining is realized by the manipulator itself and due to this "in-situ" technique created grippers are exempt of misalignment errors. We have shown that precise micromachining of micro-parts or microstructures can be realized with a compact equipment. A microfactory concept has been introduced, in order to apply the "in-situ machining" technique to an automated and low cost clean-room manufacturing process. Issues for this application, particularly in term of contamination, have been discussed

The optimal use of vision as part of the manipulation of micron-sized objects

This project concerns the development and integration of a sub-millimeter objects manipulation setup and will take part in a CTI project named “Manipulating Microscale Objects with Nanoscale Precision”. On the way of manipulating microscale objects, we need to build a first setup adapted for sub-millimeter objects in order to be able to perform experiences and to validate some assumptions and choices. In the Laboratoire de Systèmes Robotiques (LSRO), ultra high precision parallel robots are developed and, in particular, the Delta3 a micromanipulator that presents three degrees of freedom (XYZ) and has a range of 4mm. This robot might be used within this project. The goal of this project is to develop an interface between vision system, robot and user that will allow measuring the position repeatability of different microscale objects during a manipulation task

Characterization of micro manipulation tasks operated with various controlled conditions by microtweezers

Micro manipulation tasks with micro tweezers were operated in different configurations. This paper discusses the main issues of pick and place operations with micro tweezers as geometric consideration, grasping force and quality of the contact surfaces. This study is based on positioning repeatability measurements and success rate of the tasks operated automatically on our micro manipulation setup. Results for a MEMS micro gripper show a high reliability of more than 90% of success rate and positioning repeatability under the micrometer

Transformer-based normative modelling for anomaly detection of early schizophrenia

Despite the impact of psychiatric disorders on clinical health, early-stage diagnosis remains a challenge. Machine learning studies have shown that classifiers tend to be overly narrow in the diagnosis prediction task. The overlap between conditions leads to high heterogeneity among participants that is not adequately captured by classification models. To address this issue, normative approaches have surged as an alternative method. By using a generative model to learn the distribution of healthy brain data patterns, we can identify the presence of pathologies as deviations or outliers from the distribution learned by the model. In particular, deep generative models showed great results as normative models to identify neurological lesions in the brain. However, unlike most neurological lesions, psychiatric disorders present subtle changes widespread in several brain regions, making these alterations challenging to identify. In this work, we evaluate the performance of transformer-based normative models to detect subtle brain changes expressed in adolescents and young adults. We trained our model on 3D MRI scans of neurotypical individuals (N=1,765). Then, we obtained the likelihood of neurotypical controls and psychiatric patients with early-stage schizophrenia from an independent dataset (N=93) from the Human Connectome Project. Using the predicted likelihood of the scans as a proxy for a normative score, we obtained an AUROC of 0.82 when assessing the difference between controls and individuals with early-stage schizophrenia. Our approach surpassed recent normative methods based on brain age and Gaussian Process, showing the promising use of deep generative models to help in individualised analyses.Comment: 10 pages, 2 figures, 2 tables, presented at NeurIPS22@PAI4M

An automated machine learning approach to predict brain age from cortical anatomical measures

The use of machine learning (ML) algorithms has significantly increased in neuroscience. However, from the vast extent of possible ML algorithms, which one is the optimal model to predict the target variable? What are the hyperparameters for such a model? Given the plethora of possible answers to these questions, in the last years, automated ML (autoML) has been gaining attention. Here, we apply an autoML library called Tree-based Pipeline Optimisation Tool (TPOT) which uses a tree-based representation of ML pipelines and conducts a genetic programming-based approach to find the model and its hyperparameters that more closely predicts the subject's true age. To explore autoML and evaluate its efficacy within neuroimaging data sets, we chose a problem that has been the focus of previous extensive study: brain age prediction. Without any prior knowledge, TPOT was able to scan through the model space and create pipelines that outperformed the state-of-the-art accuracy for Freesurfer-based models using only thickness and volume information for anatomical structure. In particular, we compared the performance of TPOT (mean absolute error [MAE]: 4.612 ± .124 years) and a relevance vector regression (MAE 5.474 ± .140 years). TPOT also suggested interesting combinations of models that do not match the current most used models for brain prediction but generalise well to unseen data. AutoML showed promising results as a data-driven approach to find optimal models for neuroimaging applications