Distributed and Dynamic Map-less Self-reconfiguration for Microrobot Networks

International audienceMEMS micro robots are low-power and low memory capacity devices that can sense and act. One of the most challenges in MEMS micro robot applications is the self-reconfiguration, especially when the efficiency and the scalability of the algorithm are required. In the literature, if we want a self-reconfiguration of micro robots to a target shape consisting of P positions, each micro robot should have a memory capacity of P positions. Therefore, if P equals to millions, each node should have a memory capacity of millions of positions. Therefore, this is not scalable. In this paper, nodes do not record any position, we present a self-reconfiguration method where a set of micro robots are unaware of their current position and do not have the map of the target shape. In other words, nodes do not store the positions that build the target shape. Consequently, memory usage for each node is reduced to O(1). An algorithm of self-reconfiguration to optimize the communication is deeply studied showing how to manage the dynamicity (wake up and sleep of micro robots) of the network to save energy. Our algorithm is implemented in Meld, a declarative language, and executed in a real environment simulator called DPRSim

Planning and control for microassembly of structures composed of stress-engineered MEMS microrobots

We present control strategies that implement planar microassembly using groups of stress-engineered MEMS microrobots (MicroStressBots) controlled through a single global control signal. The global control signal couples the motion of the devices, causing the system to be highly underactuated. In order for the robots to assemble into arbitrary planar shapes despite the high degree of underactuation, it is desirable that each robot be independently maneuverable (independently controllable). To achieve independent control, we fabricated robots that behave (move) differently from one another in response to the same global control signal. We harnessed this differentiation to develop assembly control strategies, where the assembly goal is a desired geometric shape that can be obtained by connecting the chassis of individual robots. We derived and experimentally tested assembly plans that command some of the robots to make progress toward the goal, while other robots are constrained to remain in small circular trajectories (orbits) until it is their turn to move into the goal shape. Our control strategies were tested on systems of fabricated MicroStressBots. The robots are 240–280 µm × 60 µm × 7–20 µm in size and move simultaneously within a single operating environment. We demonstrated the feasibility of our control scheme by accurately assembling five different types of planar microstructures

Algorithmes distribués pour l'optimisation de déploiement des microrobots MEMS

MEMS microrobots are miniaturized elements that can capture and act on the environment. They have a small size, low memory capacity and limited energy capacity. These inexpensive devices can perform several missions and tasks in a wide range of applications such as locating odor, fighting against fires, medical service, surveillance, search, rescue and safety. To do these tasks and missions, they have to carry out protocols of redeployment to adapt to the working conditions. These algorithms should be efficient, scalable, robust and should only use local information. Redeployment for mobile MEMS microrobots currently requires a positioning system and a map (predefined positions) of the target shape. Traditional positioning solutions such as using GPS consumes a lot of energy and it is no applicable in the micro scale. Also, the use of an algorithmic solution positioning with multilateration techniques causes problems due to errors in the coordinates obtained. In the literature works, if we want a microrobots self-reconfiguring to a target shape consisting of P positions, each microrobot must have a storage capacity of at least P positions to save them. Therefore, if P equals to thousands or millions, every node must have a storage capacity of thousands or millions of positions. However, these algorithms are notscalable. In this thesis, we propose protocols of self-reconfiguration where nodes are not aware of their position in the plane and do not record the positions of the target shape. Therefore, the memory space required for each node is significantly reduced at a constant complexity. The purpose of these distributed algorithms is to optimize the logical topology of the network of mobile MEMS microrobots to seek a better complexity for message exchange and inexpensive communication.In this work, we show for the reconfiguration of a chain into a square, how to handle the dynamicity of the network to save energy, and we study how to use parallelism in motion to optimize the execution time and the number of movements. Furthermore, another solution is proposed where the initial physical topology may be any connected configuration. With thesesolutions the nodes can execute the algorithm regardless of where they are deployed, because the algorithm is independent of the map of the target shape. Furthermore, these solutions seek to achieve the shape of the target with a minimum amount of movement.Les microrobots MEMS sont des éléments miniaturisés qui peuvent capter et agir sur l'environnement. Leur taille est de l'ordre du millimètre et ils ont une faible capacité de mémoire et une capacité énergétique limitée. Les microrobots MEMS continuent d'accroître leur présence dans notre vie quotidienne. En effet, ils peuvent effectuer plusieurs missions et tâches dans une large gamme d'applications telles que la localisation d'odeur, la lutte contre les incendies, le service médical, la surveillance, le sauvetage et la sécurité. Pour faire ces taches et missions, ils doivent appliquer des protocoles de redéploiement afin de s'adapter aux conditions du travail. Ces algorithmes doivent être efficaces, évolutifs, robustes et ils doivent utiliser de préférence des informations locales. Le redéploiement pour les microrobots MEMS mobiles nécessite actuellement un système de positionnement et une carte (positions prédéfinies) de la forme cible. La solution traditionnelle de positionnement comme l'utilisation d'un GPS consommerait trop d'énergie. De plus, l'utilisation de solutions de positionnement algorithmique avec les techniques de multilatération pose toujours des problèmes à cause des erreurs dans les coordonnées obtenues.Dans la littérature, si nous voulons une auto-reconfiguration de microrobots vers une forme cible constituée de P positions, chaque microrobot doit avoir une capacité mémoire de P positions pour les sauvegarder. Par conséquent, si P est de l'ordre de milliers ou de millions, chaque noeud devra avoir une capacité de mémoire de positions en milliers ou millions. Parconséquent, ces algorithmes ne sont pas extensibles ou évolutifs. Dans cette thèse, on propose des protocoles de reconfiguration où les noeuds ne sont pas conscients de leurs positions dans le plan et n'enregistrent aucune position de la forme cible. En d'autres termes, les noeuds ne stockent pas au départ les coordonnées qui construisent la forme cible. Par conséquent, l'utilisation de mémoire pour chaque noeud est réduite à une complexité constante. L'objectif desalgorithmes distribués proposés est d'optimiser la topologie logique du réseau des microrobots afin de chercher une meilleure complexité pour l'échange de message et une communication peu coûteuse. Ces solutions sont complètement distribués. On montre pour la reconfiguration d'une chaîne à un carré comment gérer la dynamicité du réseau pour sauvegarder l'énergie, on étudie comment utiliser le parallélisme de mouvements pour optimiser le temps d'exécution et lenombre de mouvements. Ainsi, on propose une autre solution où la topologie physique initiale peut être n'importe quelle configuration initiale. Avec ces solutions, les noeuds peuvent exécuter l'algorithme indépendamment du lieu où ils sont déployés, parce que l'algorithme est indépendant de la carte de la forme cible. En outre, ces solutions cherchent à atteindre la forme de la cible avec une quantité minimale de mouvement

Four dof Piezoelectric Microgripper Equipped with a Smart CMOS Camera.

International audienceThis paper deals with the design of a micro-eyein- hand architecture. It consists of a smart camera embedded on a gripper. The camera is a high speed (10 000 fps) CMOS sensor of 64 64 pixels. Each pixel measures 35 m 35 m and includes a photodiode, an amplifier, two storage capacitors, and an analog arithmetic unit. The gripper consists of a 4 dof (degrees-of-freedom) (y+, y-, z+, z-) microprehensile based on piezoelectric actuators

Electrostatic microgenerators

Accepted versio

An Untethered Miniature Origami Robot that Self-folds, Walks, Swims, and Degrades

A miniature robotic device that can fold-up on the spot, accomplish tasks, and disappear by degradation into the environment promises a range of medical applications but has so far been a challenge in engineering. This work presents a sheet that can self-fold into a functional 3D robot, actuate immediately for untethered walking and swimming, and subsequently dissolve in liquid. The developed sheet weighs 0.31g, spans 1.7cm square in size, features a cubic neodymium magnet, and can be thermally activated to self-fold. Since the robot has asymmetric body balance along the sagittal axis, the robot can walk at a speed of 3.8 body-length/s being remotely controlled by an alternating external magnetic field. We further show that the robot is capable of conducting basic tasks and behaviors, including swimming, delivering/carrying blocks, climbing a slope, and digging. The developed models include an acetone-degradable version, which allows the entire robot’s body to vanish in a liquid. We thus experimentally demonstrate the complete life cycle of our robot: self-folding, actuation, and degrading.National Science Foundation (U.S.) (Grant 1240383)National Science Foundation (U.S.) (Grant 1138967)American Society for Engineering Education. National Defense Science and Engineering Graduate Fellowshi

Efficient Simulation Environment of Wireless Radio Communications in MEMS Modular Robots

International audienceModular robots needs networking for coordination and it is particularly true for MEMS micro robots. A promising communication technology is nanowireless networking which could be integrated directly into MEMS micro robots, in our case, the catoms of the Claytronics project. A first step towards this objective is to design a wireless simulator able to deal with modular robots. This simulator called Vouivre is integrated in DPRSim a modular robot simulator developed by Intel Research. This paper describes Vouivre and its integration in DPRSim which is an interesting case of integrating different timelines in one simulator. Experiments validate our design and show the interest of using wireless communication in modular robots

Automated Real-Time Control of Fluidic Self-Assembly of Microparticles

Self-assembly is a key coordination mechanism for large multi-unit systems and a powerful bottom-up technology for micro/nanofabrication. Controlled self-assembly and dynamic reconfiguration of large ensembles of microscopic particles can effectively bridge these domains to build innovative systems. In this perspective, we present SelfSys, a novel platform for the automated control of the fluidic self-assembly of microparticles. SelfSys centers around a water-filled microfluidic chamber whose agitation modes, induced by a coupled ultrasonic actuator, drive the assembly. Microparticle dynamics is imaged, tracked and analyzed in real-time by an integrated software framework, which in turn algorithmically controls the agitation modes of the microchamber. The closed control loop is fully automated and can direct the stochastic assembly of microparticle clusters of preset dimension. Control issues specific to SelfSys implementation are discussed, and its potential applications presented. The SelfSys platform embodies at microscale the automated self-assembly control paradigm we first demonstrated in an earlier platform