Dispensing sessile droplets accurately on the top of the needle tip is important for applications such as fiber threading. In this paper, we propose an accurate online gel dispensing method to accurately dispense sessile droplets on top of the dispenser tip for highly repeatable fiber threading. We design a robotic gel dispenser based on positive-displacement piston dispensing that can accurately dispense sessile gel droplets of desired volume using visual servoing and adaptive model predictive control. An online gel volume estimation algorithm based on image processing is constructed to provide the estimation of the volume of the extruded gel droplet to the controller. To compensate for the nonlinear and time-varying process properties, the Adaptive Model Predictive Controller (MPC) adjusts its prediction model at runtime while satisfying a set of constraints. We employ the fiber threading technique based on impedance control with force tracking we developed recently to carry out the fiber threading experiments. To examine the benefits of the accurately dispensed droplet, multiple fibers threading experiments are conducted, where the repeatability of the fibers fabricated using the proposed methods are compared with two other methods: a) conventionally velocity regulation-based fiber fabrication, where the pulling force profile is not controlled, and b) fiber threading using impedance control with force tracking using a commercial time-pressure dispenser. The experimental results show that fibers fabricated using the proposed method have the highest repeatability based on the coefficient of variation of properties of the fabricated fibers, where the obtained coefficient of variation of the toughness, stiffness, elongation, and strength are 6.7%, 3.7%, 3.4%, 5.6% respectively.

This article proposes an original approach for robotic calibration that is based on measurements along a single direction (1-D). Among all applications, the field of microrobotics and nanorobotics has been chosen as case study because of the strong needs for high positioning accuracy (10–100 nm typically) while measuring with sufficient resolution along multi-degrees of freedom (DoF) is still a fully open question. The 1-D measurements relying on Fabry–Perot (FP) interferences is used, and the proposed modeling of a 6-DoF nanopositioning robot enables to derive the measurement strategy as well as the identification procedure for both extrinsic and intrinsic parameters. Experimental investigations demonstrate that the approach is easy to implement, low cost, and enables to understand what are the main influential parameters onto positioning accuracy. They also conduct to very high accuracy in 6-DoF positioning; a positioning accuracy estimate of 50 nm and 0.004° has notably been obtained for the full pose (position and orientation, respectively) and can be held during several hours after the measurements.Note to Practitioners—The motivation of this work is to give an answer to the growing needs for micro nanopositioning robots having a very high precision for applications in microassembly, in the characterization of micro and nano components or biological elements, or for minimally invasive surgery. The key contribution of the proposed work relies on the method proposed that enables to efficiently calibrate a serial 6-degree-of-freedom (DoF) robot using only 1-D interferometric measurements, which overcomes the need for multi-DoF, small size, and high-resolution sensors missing in commercial offers. Moreover, the behavior of such novel robotic solutions is not well known, difficult to model, and there is a clear lack of knowledge about the main influential parameters. The method proposed in this article notably enables to achieve the full calibration of the robot (all intrinsic and extrinsic parameters identified) to reach the best positioning accuracy. This first part takes times, and this article shows that it is then possible to adopt a less time taking procedure by only reidentifying extrinsic parameters and thus to hold these performances over long periods, typically a week.

In this paper, we focus on the problem of optimally placing a mixture of static and PTZ cameras based on the resolution requirement, this configuration will be useful later cameras planning. The static cameras used for detecting an object or an event, this result is used to select the best PTZ camera within the network to identify or recognize this moving object or event. In our work the monitoring area is represented by a grid of points distributed uniformly or randomly (S. Thrun, 2002), then using surface-projected monitoring area and camera sensing model we develop a binary integer programming algorithm. The results of the algorithm are applied successfully to a variety of simulated scenarios.