At small scales, automating robotic tasks such as assembly, force/displacement characterization, positioning, etc., appear to be particularly limited. This is due to the lack of sufficiently performing and easy-to-implement multi-degrees-of-freedom measurement systems able to measure the relative pose between micro-parts. In order to address this issue, a measurement method based on High-Precision fiducial markers (named HP code) is proposed. This measurement method combines a periodic pattern (providing high resolution by phase-based computation) with more regular QR codes (bringing versatile implementations and a quick detection). The design and method to efficiently locate these HP codes are presented in this paper. Experimental investigations demonstrate ultra-high resolution: 2 nm and$5 ~\mu $rad along$X,Y$and$\Theta $respectively (i.e. one thousandth of a pixel typically). The method is designed to be scalable as well as self-calibrated and to provide high robustness and high versatility. Two typical challenging applications in the field of microrobotics are automated to demonstrate these disruptive performances and the easy-to-implement capability of the method: (1) the automated assembly of two micro-fluidic chips through visual servoing with an achieved positioning accuracy below 50 nm, and (2) the automated micromechanical characterization of single fibers achieved by the integration of HP codes into a compliant structure enabling simultaneous micro-force and displacement sensing capabilities. These achievements highlight the versatility of the method and open the door to the rapid automation of high-quality robotic tasks at the micro scale. Note to Practitioners—The motivation for this work/study is based on the fact that many application areas are extensively orienting towards microrobotic systems to perform precise tasks with versatility. However, at the micro scale, many disturbances such as the effects of climate change strongly affect this precision. This problem is amplified by the fact that sensors cannot be easily integrated, either by lack of space or by the lack of measurement systems available. Vision-based approaches are widespread at this scale and appear very promising to measure the relative pose between micro-parts. Nevertheless, existing vision-based approaches like digital image correlation are both scale and texture dependent. Due to the lack of space, they are also difficult to use in practice at small scales for high resolution measurement. The main contribution of this paper lies in the capability to achieve ultra-high resolution measurements. For that, a structure based on High-Precision fiducial markers (named HP codes) is proposed and requires few and simple settings while achieving very high resolution both in position and orientation, typically down to one thousandth of a pixel and a few micro radians, respectively. It provides an off-the-shelf solution, versatile, easy to implement and achieves high resolution measurements in the plane (XY$\Theta $). HP codes are applicable to a wide range of applications such as tracking of a component/part of a mobile or deformable system, visual servoing of microrobots, positioning of samples, assembly of components or even mechanical characterization. A free distribution of the library is available online athttps://projects.femto-st.fr/vernier/.

Concentric tube robots (CTRs) have drawn significant research attention over the years, particularly due to their applications in minimally invasive surgery (MIS). Indeed, their small size, flexibility, and high dexterity enable several potential benefits for MIS. The research has led to an increasing number of discoveries and scientific breakthroughs in CTR design, fabrication, control, and applications. Numerous prototypes have emerged from different research groups, each with its own design and specifications. This survey paper provides an overview of the state of the art of the mechatronics aspects of CTRs, including approaches for the design and fabrication of the tubes, actuation unit, and end effector. In addition to the various hardware and associated fabrication methods, we propose to the research community, a unifying way of classifying CTRs based on their actuation unit architecture, as well as a set of specification details for the evaluation of future CTR prototypes. Finally, we also aim to highlight the current advancements, challenges, and perspectives of CTR design and fabrication.

The demand in the biomedical field for fast and precise devices forin vitroapplications has increased in recent years. Mobile microrobots are significantly suitable for such applications and are developing rapidly. These microrobots offer untethered actuation toward a contamination-free environment while allowing for fast and precise handling of biological entities for applications such as positioning, sensing, delivery, and cell surgery that are highly effective for new drug discoveries and improving our understanding of cells’ behavior on the single-cell level. Here, we present a review of the recent state of the art in the actuation and implementation of mobile microrobots forin vitroapplications. We first explore the widely used methods of wireless actuation. Next, we address the challenge of implementing an on-board interaction technique to handle the target biological entity without affecting the actuation of the microrobot. Finally, we discuss the future directions that would draw the basic outline for the next generation of mobile microrobots forin vitroapplications.

Parallel Continuum Robots (PCR) have received a lot of attention in recent years. This paper presents a new 6-degrees-of-freedom PCR derived from the conventional 3-PPSR parallel manipulator. This robot is driven by three limbs consisting of two flexible rods each and replacing the spherical and revolute joints of the original version. Each limb is mounted onto two linear axes arranged in series. To allow a direct comparison between the articulated and the continuum version, the parallel mechanism of an industrial manipulator has been replaced by an elastic structure of the same size. The simulations and the experiments show that the flexible counterpart of the manipulator is able to achieve a larger workspace, increasing the range of motion by 150% for rotations and by 157% in elevation. Moreover, the position repeatability is improved by 47% (reaching 3.4 µm) and the orientation repeatability by 57% (reaching 14.3 µrad). This can be explained by the removal of the spherical and revolute joints but also by the constant stress in the structure that acts as an anti backlash system on leadscrew actuators.

Two-fingered manipulation robotic systems are widely used in many application sand notably at small scales. The commonly employed solution is based on the attachment of a gripper on a robot. This paper introduces a new eight degrees of freedom (DoF) mechanism intended for two-fingered dexterous manipulation. The eight DoFs are obtained via a parallel architecture moved by eight actuators that are attached to the base of the robot. The novelty lies in the use of a 2-DoF configurable platform that insures two relative rotations of the gripper jaws (opening and twisting) in addition to the 6-DoF Cartesian movements. The paper presents the kinematics and the design of a proof-of-concept able to grasp, roll, translate and rotate objects in a single compact design. A prototype is operated to manipulate and insert a 2-mm screw.

This paper deals with an OCT (optical coherence tomography) geometric calibration method. OCT medical imaging system has received a growing interest during the last two decades. In medical purposes, OCT images are generally called optical biopsies which allows in-vivo investigation almost similar to a histopathological study. The physician can rely on the OCT images to establish a rapid and direct diagnosis. But the OCT images formation suffered numerous distortions due in particular to the optical path, from the source to the viewed sample passing through the two reflecting mirrors and a scan objective. The obtained optical biopsies include several spectral and geometric distortions. The proposed calibration model aims to compensate the geometrical ones. More precisely, two models were developed allowing the correction of both 2D images (B-Scan slices) and 3D images (volume). These models were experimentally validated (in both artificial and biological samples) using a spectral domain OCT system. It has demonstrated a significant enhancement of the OCT images accuracy.

This paper presents preliminary results on OCT (Optical Coherence Tomography) based control of a concentric tube robot (CTR). The potential use of OCT system is presented then its ability to perform an optical biopsy and an eye-to-hand configuration for robot control is highlighted. A prototype of CTR with three curved tubes is shortly described then a model-less OCT-based controller is detailed. The extraction of visual features from OCT images is performed by normalized cross-correlation. The proposed control scheme was experimentally validated. The positioning accuracy of CTR control was evaluated in benchtop trials. The error in tip placement can reach 10 μm along X and 74 μm along Y according to controller parameters. The 2D positioning (XY plan) is also tested then a final error about 55 μm is achieved.

Recent studies show that the handling of thin wafers in the photovoltaic industry can lead to unacceptable yields due to cell scratching and breaking. This paper presents the concept and design of a novel modular conveyor that is intended for handling planar fragile objects at a high speed without contact. Each element of the conveyor is a square block that is able to generate tilted air jets that lift and push the object in a single direction. Various handling functions can thus be achieved by the assembly of several blocks. To manage the complexity of assembling systems composed of hundreds of blocks, an automatized design methodology is proposed. This process gives the best topology of the conveyor that meets expected specifications such as trajectory, speed, and travel time. The optimization relies on the physical model of the modular system describing the motion of the object pushed by directed air jets. Experimental comparisons show that the simulation predicts accurately the motion of a glass wafer according to the arrangement of the blocks and the volume of air flow. A maximal speed of 0.3 m/s was experimentally observed and, on larger simulated conveyors, the speed of the wafer could theoretically reach 2.9 m/s.

This paper proposes the design of a vision-guided control law for microrobotic-assisted biomedical applications. More precisely, the developed control law is to servo an optical coherence tomography (OCT) system in real-time to carry out repetitive optical biopsy tasks. The OCT images are simultaneously used to perform the optical biopsy and to control the sample holder (microrobotic work-flow). Instead of extracting visual features from the OCT images, the vision-based controller uses the concept of frequency domain information to compute relative motion between two successive OCT B-scan (cross-section) images. Therefore, the visual controller is designed to minimize the error between current and desired images by controlling the microrobotic platform. The proposed approach was experimentally validated, demonstrating more than satisfactory results especially in terms of accuracy, convergence, and robustness.

Recently, high resolution visual methods based on direct-phase measurement of periodic patterns has been proposed with successful applications to microrobotics. This paper proposes a new implementation of direct-phase measurement methods to achieve 3-DoF (degrees of freedom) visual servoing. The proposed algorithm relies on a single frequency tracking rather than a complete 2D discrete Fourier transform that was required in previous works. The method does not require any calibration step and has many advantages such as high subpixelic resolution, high robustness and short computation time. Several experimental validations (in favorable and unfavorable conditions of use) were performed using a XYθ microrobotic platform. The obtained results demonstrate the efficiency of the frequency-based controller, this in term of accuracy (micrometric error), convergence rate (30 iterations in nominal conditions) and robustness.

This paper presents preliminary results of a new robotic wrist for minimally invasive surgery. This wrist is a high-dexterity miniature robot, able to provide simultaneously the grasping/cutting (1-DoF) and rotations capabilities with 3-DoF. The grasping function is insured by the folding of the top platform of a parallel structure. The grasping capability of the wrist is part of the mechanical structure itself and can be fully controlled by external actuators. In order to validate this original approach, an experimental prototype has been fabricated using 3D printing technology at a larger scale. The inverse kinematic model has been developed and the workspace analysis was accomplished to assess the capabilities of the realized system. Finally, experimental tests have been also carried out for validating the proposed structure.

In the photovoltaic solar cell industry as in the semiconductor industry, efforts to reduce the thickness of silicon wafers are in progress. Wafer damage and breakage during handling can lead to unacceptable yields and alternative solutions have to be proposed. This paper presents a modular contact-free wafer handling system that responds to the industrial requirements in terms of throughput and flexibility. The system is based on simple unidirectional modules that can be assembled together to form the desired trajectory. A complete and accurate physical model of the modular system describing the motion of the wafer transported by directed air-jets is proposed. A decentralized control at the block level is realized to damp the object motion. The experimental results show a great reduction of the response time compared to free motion and a standard deviation of the servo error below the millimeter. In addition, simulations show that a 150 mm wafer could reach a speed up to 2.9 m/s on large conveyors.

Microassembly represents a very promising solution to microproducts and complex Micro-Electro-Mechanical Systems (MEMS) fabrication. Since, in the case of teleoperated assembly, an operator is the main source of errors, there is a great interest in microassembly automation. Its main issue consists in precise estimation of object position. Previous studies demonstrate the possibility of application of modelbased visual tracking algorithms from ViSP (Visual Servoing Platform) library. However, the methods of macroassembly cannot be directly applied when working with microobjects. The characterization of single-view visual tracking notably revealed the complexity of depth estimation in microscale, which is due to small depth variation in the seen images compared with the distance from camera. So, an algorithm of Z coordinate reconstruction using a second camera was developed and analyzed for visual servoing task. It was then used to automate microassembly. Experiments demonstrate the possibility of complex microcomponent automatic microassembly with precision better than 10 micrometers.

High positioning accuracy with micropositioning robots (MPRs) is required to successfully perform many complex tasks, such as microassembly, manipulation, and characterization of biological tissues and minimally invasive inspection and surgery. Despite the widespread use of high-resolution micro- and nanopositioning robots, there is very little knowledge about the real positioning accuracy that can be obtained and what the main influential factors are. Indeed, very few notable methods are available to measure multi-degree-of-freedom motions with adapted range, resolution, and dynamic capabilities. The main objective of this paper is to quantify the positioning accuracy of serial MPRs and to identify the main influential factors (a typical XY Θ serial robot is chosen as a case study). To reach this goal, a measuring system that combines vision and pseudoperiodic patterns with an extremely large range-to-resolution ratio is introduced as a new way to quantify the positioning accuracy of MPRs for in-plane motions. Then, an open-loop control approach based on MPR calibration is chosen for several reasons: the use of different models to identify influential factors, the quantification of the positioning accuracy, and the necessity of the method when sensor integration is too complex. Experiments using five different calibration models were conducted to classify factors influencing the positioning accuracy of MPRs. The results show that positioning accuracy can be improved by more than 35 times from 96 μ with no imperfection compensation to 2.5 μ by compensating for geometric, position-dependent, and angle-dependent errors through the MPR calibration approach.

Accuracy is an important issue for microrobotic applications. High accuracy is usually a necessary condition for reliable system performance. However there are many sources of inaccuracy acting on the microrobotic systems. Characterization and compensation enable reduction of the systematic errors of the micropositioning stages and improve the positioning accuracy. In this paper, we propose a novel method based on vision and pseudo-periodic encoded patterns to characterize the position-dependent errors along XY stages. This method is particularly suitable for microscale motion characterization thanks to its high range-to-resolution ratio and avoidance of camera calibration. Based on look-up tables and interpolation techniques, we perform compensation and get improved accuracy. The experimental results show an accuracy improved by 84% for square tracking and by 68% for random points reaching (respectively from 22 μm to 3.5 μm and from 22 μm to 7 μm).

Many industries require contactless transport and positioning of delicate or clean objects such as silicon wafers, glass sheets, solar cell or flat foodstuffs. The authors have presented a new form of contactless distributed manipulation using induced air flow. Previous works concerned the evaluation of the maximal velocity of transported objects and one degree-of-freedom position control of objects. This paper introduces an analytic model of the velocity field of the induced air flow according to the spatial configuration of vertical air jets. Then two degrees-of-freedom position control is investigated by exploiting the linearity property of the model. Finally the model is validated under closed-loop control and the performances of the position control are evaluated.

This paper introduces a new aerodynamic-traction principle to handle delicate and clean products, such as silicon wafers, glass sheets, or flat foodstuffs. The product is carried on a thin air cushion and transported along the system by induced airflows. This induced airflow is an indirect effect of strong vertical air jets that pull the surrounding fluid. This paper provides a qualitative explanation of the operating principles and a description of the experimental device. First experimental results with active control are presented. The maximum velocity and acceleration that can be obtained for the considered device geometry meet the requirements for industrial applications.

This paper presents a distributed control architecture to perform part recognition and closed-loop control of a distributed manipulation device. This architecture is based on decentralized cells able to communicate with their four neighbors thanks to peer-to-peer links. Various original algorithms are proposed to reconstruct, recognize and convey the object levitating on a new contactless distributed manipulation device. Experimental results show that each algorithm does a good job for itself and that all the algorithms together succeed in sorting and conveying the objects to their final destination. In the future, this architecture may be used to control MEMS-arrayed manipulation surfaces in order to develop Smart Surfaces, for conveying, fine positioning and sorting of very small parts for micro-systems assembly lines.

In this paper, a new contactless conveyor system based on an original aerodynamic traction principle is described and experimented. This device is able to convey without any contact flat objects like silicon wafer, glass sheets or foodstufff thanks to an air cushion and induced air flows. A model of the system is established and the identification of the parameters is carried out. A closed-loop control is proposed for one dimension position control and position tracking. The PID-controller gives good performances for different reference signals. Its robustness to object change and perturbation rejection are also tested.

Recently, a great deal of interest has been developed in learning in multi-agent systems to achieve decentralized control. Machine learning is a popular approach to find controllers that are tailored exactly to the system without any prior model. In this paper, we propose a semi-decentralized reinforcement learning control approach in order to position and convey an object on a contact-free MEMS-based distributed-manipulation system. The experimental results validate the semi-decentralized reinforcement learning method as a way to design control laws for such distributed systems.

Multi-agent systems (MAS) are a field of study of growing interest in a variety of domains such as robotics or distributed controls. The article focuses on decentralized reinforcement learning (RL) in cooperative MAS, where a team of independent learning robots (IL) try to coordinate their individual behavior to reach a coherent joint behavior. We assume that each robot has no information about its teammates' actions. To date, RL approaches for such ILs did not guarantee convergence to the optimal joint policy in scenarios where the coordination is difficult. We report an investigation of existing algorithms for the learning of coordination in cooperative MAS, and suggest a Q-learning extension for ILs, called hysteretic Q-learning. This algorithm does not require any additional communication between robots. Its advantages are showing off and compared to other methods on various applications: bi-matrix games, collaborative ball balancing task and pursuit domain.

Reinforcement learning (R-L) is an intuitive way of programming well-suited for use on autonomous robots because it does not need to specify how the task has to be achieved. However, RL remains difficult to implement in realistic domains because of its slowness in convergence. In this paper, we develop a theoretical study of the influence of some RL parameters over the learning speed. We also provide experimental justifications for choosing the reward function and initial Q-values in order to improve RL speed within the context of a goal-directed robot task

This paper presents a new reinforcement learning algorithm named Short Term Model-based learning (STM-Q) able to learn quickly to control stochastic systems. Our objective is to control the positioning of small mechanical parts with a micromanipulation system for microfactory applications. The micropositioning platform is made of three linear actuators that move points to push the object toward a goal position. At the micrometric scale, it is really difficult to model the behaviour of manipulated objects. Moreover, due to the gap between real world and discrete world perceived by the camera, the motion of parts can be seen as a little stochastic. To overcome these problems and design an automated device, we use reinforcement learning methods and develop a new model-based algorithm taking into account the stochastic behaviour of the positioning task.

This paper presents a reinforcement learning algorithm based on a parallel approach of the Watkins's Q-learning. This algorithm is used to control a two axis micro-manipulator system. The aim is to learn complex behaviour such as reaching target positions and avoiding obstacles at the same time. The simulations and the tests with the real manipulator show that this algorithm is able to learn simultaneously opposite behaviours and that it generates interesting action policies with regard to global path optimization.

We present a survey of fish-like propulsion at millimeter scale in order to build high efficiency swimming microrobots. We begin with a mechanical study of the fish-like propulsion. The mechanical model we used shows that undulatory motions are more efficient than oscillatory motions. We applied these theoretical results to the design and the realization of a microrobot propelled by the beating of two fins. Fins are moved by a transducer material called IPNC (ionic polymer composite metal). The experimental results allow us to check our theoretical model of the microrobot. Finally, we propose an improved microrobot which would have a better efficiency.

This paper presents an application of reinforcement learning to a block-pushing problem. The manipulator system we used is able to push millimeter size objects on a glass slide under a CCD camera. The objective is to automate high level tasks of pushing. Our approach is based on reinforcement learning algorithm (Q-learning) because the models of the manipulator and of the dynamics of objects are unknown. The system is too complex for a classic algorithm, so we propose an original architecture which realizes several learning processes at the same time. This method produces an almost optimal policy whatever the number of manipulated objects may be. Some simulations allowed us to optimize every parameter of the learning process. In particular, they show that the more objects there are, the faster the controller learns. The experimental tests show that, after the learning process, the controller fills his part perfectly.