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/.

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.

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).