Multi-thread flexible microelectrodes hold the promise of high-quality and long-term neural signal recording. To implant fragile microelectrodes into brain, a microneedle is usually used as a shuttle to increase stiffness. However, hooking microelectrodes with a microneedle is error-prone and time-consuming, due to the difficulty in accurately obtaining micro-object coordinates through low-quality microscope images. To solve this problem, this paper proposes an intelligent microscope vision system based on few-shot segmentation method. Firstly, a stereo vision system is designed to enable cameras to focus on all microelectrode threads simultaneously. Secondly, a few-shot instance segmentation method is proposed to obtain key-point coordinates for vision guidance, which consists of data augmentation, improved Mask-RCNN and anchor-free clustering loss. The data augmentation method synthesizes low-quality images to strengthen model’s generalization ability. To improve the proposal quality, a residual region proposal module is introduced into Mask-RCNN. By clustering instance features in metric space, the anchor-free clustering loss enhances the model capability of predicting hard instances and avoids the intra-class bias of fixed anchor. Experimental result shows that the proposed method achieves 99.72% and 90% correct rate for microelectrode and microneedle segmentation with 5-shot training. Code is available athttps://github.com/NamingIsEasy/FSS_implantation. Note to Practitioners—Multi-thread flexible microelectrodes are an emerging brain-computer interface platform. Compared with traditional electrodes, they have the advantages of low elastic modulus, micron-level width and material biocompatibility. However, the low elastic modulus also makes it difficult to be inserted into brain. Thus, researchers proposed “sewing machine” paradigm, in which a microneedle is used to hook microelectrodes and drive them to be inserted into brain. The hooking process is threading the microneedle into engaging holes at the end of microelectrodes. However, undesirable conditions in microscope images, such as out-of-focus blur and occlusion, make it difficult to accurately obtain microneedle and microelectrode coordinates. Towards automated hooking operation, this paper proposed an intelligent microscope vision system based on few-shot segmentation. Firstly, a stereo vision system is designed to focus cameras on all microelectrode threads simultaneously. Secondly, a few-shot segmentation method is proposed to reduce time of image acquisition and annotation process. It consists of data augmentation, improved Mask-RCNN and anchor-free clustering loss. The data augmentation method synthesizes undesirable-condition images to reduce the bias of training dataset. The classical region proposal module in Mask-RCNN is replaced with an improved residual version. To make the model focus more on hard instances during training, an anchor-free clustering loss is introduced. The proposed method achieves 99.72% and 90% correct rate for microelectrode and microneedle segmentation in 5-shot experiments. In future, we will explore automated hooking control and subsequent implantation process based on the proposed vision system.

Machining chatter is an unstable vibration that often results in poor surface quality, low productivity, and degraded machine tool life. Chatter detection is an important approach to timely avoid detrimental effects on the workpiece and machine tools. However, most chatter detection methods will lose effectiveness and even yield false alarms, when chatter is accompanied by the beat effect. The beat effect is a common phenomenon in the machining process, which results in severely modulated amplitude of chatter. In this article, the variable-scale wavelet packet entropy (VSWPE) is proposed to detect chatter with the beat effect based on beat frequency estimation. First, an optimal demodulation technique is presented to extract the chatter vibration component and its modulated instantaneous amplitude (IA) from measured signals contaminated with noise and periodic interference. Afterward, the beat frequency is estimated from the harmonically distorted IA by an interpolated discrete Fourier transform (DFT). Finally, the VSWPE is calculated for chatter detection at a variable scale that is adaptively computed by the beat frequency. Numerical simulations show that the beat frequency can be accurately estimated with computational efficiency. Moreover, machining tests under different cutting conditions demonstrate that the proposed chatter detection method can effectively detect chatter with the beat effect.Note to Practitioners—Automatic online chatter detection is crucial in order to allow operators to timely interfere with the process and avoid detrimental effects caused by chatter. However, the practical machining process is considerably complex especially for unstable scenarios, where the beat effect is likely to occur simultaneously. Traditional chatter detection methods neglect the presence of the beat effect, and they will give misleading results and even false alarms when the beat effect is present. This article seeks to accurately detect chatter with and without the beat effect in a unified framework. The proposed solution can be integrated into the machine tool system, hence, to enhance the productivity, reliability, and precision of the machining process.

Dynamic model has broad applications in motion planning, feedforward controller design, and disturbance observer design. Particularly, with the increasing application of model-based control in industrial robots, there has been a resurgence of research interest in accurate identification of dynamic models. However, on the one hand, most existing identification methods directly rely on least squares or weighted least squares (WLS), which suffer from outliers and could lead to physical infeasible solutions. On the other hand, nonlinearity of the friction model is seldom treated in a unified way with linear regression. Moreover, recent researches have shown that proper exciting trajectories are crucial to the identification accuracy, but few of previous works take measurement noise into consideration when optimizing the exciting trajectories. In this article, we propose an iterative approach which integrates WLS, iteratively reweighted least squares with linear matrix inequality constraints, and nonlinear friction models so that the above-mentioned issues can be properly solved altogether. Our research also reveals that performance can be improved by including priori knowledge of measurement noise in the optimization of exciting trajectories. The proposed approach is supported by experimental analysis of four different combinations within the framework on a 6-DoF industrial robot.

This paper focuses on proposing an easily-implemented control method for high-acceleration point-to-point motions. The control algorithm used here to handle the disturbances consists of a model-based feedforward controller, a pole-placement PD controller and a disturbance observer. Then, a fast time-domain identification technique is implemented to give the accurate model parameters, which can be directly utilized in the control algorithm. The method avoids the complicated parameters tuning process, which would be attractive in the industrial application. Experiments are carried out on a permanent magnet linear synchronous motor (PMLSM) and the results demonstrate that the proposed method is capable of achieving high-precision and fast positioning by reducing the tracking error and overshoot.