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Transcriptomics and metabolomics, two biological research fields that need large numbers of zebrafish embryos, require the removal of unfertilised or nonviable zebrafish embryos. Biologists routinely conduct the tedious, error-prone, and time-consuming manual sorting of embryos. We suggest a novel approach that combines deep learning and microfluidics for automated sorting to overcome this difficulty. To determine the developmental stage and viability of zebrafish eggs, we trained an optimized YOLOv5 model with 95.8% accuracy and a processing speed of 10.6 ms per frame, classifying them as dead, unfertilised, or alive. The eggs are contained in traps on a microfluidic chip using micro-pumps. After that, the deep learning system can identify and automatically sort the eggs according to their viability by positioning this chip on an XYZ motorized stage. The sorting experiment was conducted in two modes: without feedback and with feedback while using the dead egg position. The first one had a sorting success rate of 90% as opposed to 97.9% for the feedback mode with 3 seconds required for each dead egg. This automated approach provides a precise and efficient way to handle a large number of zebrafish embryos while also greatly reducing the workload associated with manual sorting. The success rates attained demonstrate the usefulness and effectiveness of our suggested methodology, opening new avenues for biological research involving accurate embryo selection.

This paper deals with a modelling technique that takes into account the effects of the temperature in the joint friction of industrial robot manipulators. In particular, it is shown that a general friction model can be suitably modified by explicitly considering the temperature as a parameter. This allows to estimate the friction term accurately in different operating conditions without the direct measurement of the joint internal temperature, which makes the overall technique suitable to apply in practical cases. Experimental results show the effectiveness of the methodology.

In this paper a practical implementation of a simplified procedure for the identification of the model of an industrial robot manipulator is presented. The approach consists of selecting a priori only the most significant parameters of the dynamic model of the manipulator and subsequently their value is identified by means of appropriate motions of the links. Then, the identified model is exploited in a model-based controller. Experimental results show how the implementation of the overall methodology can be easily performed in industrial settings, allowing a significant reduction of the trajectory tracking error.

In this paper we present an experimental comparison between different decentralized controllers for industrial robot manipulators. In spite of the fact that robot manipulators are highly nonlinear and coupled multivariable systems, decentralized controllers have been widely adopted in industrial environments for trajectory tracking, because of their simplicity and their fault tolerance feature. Different kinds of controllers have been investigated: the typical PID-based controller, a nonlinear three-term controller devised by Tarokh, and a variable structure control law, a discontinuous integral control, both in the continuous and in the discretised version. Allowances are made for the tuning of the parameters in order to obtain low tracking errors. Results show that better performances are obtained by controllers other than PID, especially by the discontinuous integral control, so that they appear to be particularly appropriate for the use in industrial robots.

The paper presents a second-order friction model for the joints of industrial robot manipulators that takes into account temperature effects. A solution based on a polynomial description of the friction is proposed. The theoretical analysis and the experimental measurements have shown that friction decreases with increasing temperature, which in turn depends on the working cycle of the manipulator. The mathematical model here proposed allows to foresee the friction variation during extensive working cycles and it does not require the use of a transducer for the measurement of the joint internal temperature; therefore it is well suitable for low-cost industrial applications, to improve the control performance or to predict the energy consumption. Experimental tests performed on a commercial six degrees-of-freedom (6 DOF) manipulator show that the model is effective in estimating the joint temperature and the friction torque during the robot operations.

In this paper a gain scheduling approach is proposed for the hybrid force/velocity control of an industrial manipulator employed for the contour tracking of objects of unknown shape. The methodology allows to cope with the configuration dependent dynamics of the manipulator during a constrained motion and therefore a significant improvement of the performance results. Experimental results obtained with an industrial SCARA manipulator demonstrate the effectiveness of the technique.