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This article presents a novel discriminative subspace-learning-based unsupervised domain adaptation (DA) method for the gas sensor drift problem. Many existing subspace learning approaches assume that the gas sensor data follow a certain distribution such as Gaussian, which often does not exist in real-world applications. In this article, we address this issue by proposing a novel discriminative subspace learning method for DA with neighborhood preserving (DANP). We introduce two novel terms, including the intraclass graph term and the interclass graph term, to embed the graphs into DA. Besides, most existing methods ignore the influence of the subspace learning on the classifier design. To tackle this issue, we present a novel classifier design method (DANP+) that incorporates the DA ability of the subspace into the learning of the classifier. The weighting function is introduced to assign different weights to different dimensions of the subspace. We have verified the effectiveness of the proposed methods by conducting experiments on two public gas sensor datasets in comparison with the state-of-the-art DA methods.

Touch modality identification has attracted increasing attention due to its importance in human-robot interactions. There are three issues involved in the tactile perception for the touch modality identification, including the high dimensionality of tactile signals, complex tensor morphology of tactile sensing units, and the misalignment among different tactile time-series samples. In this article, we propose a novel kernel-based approach to deal with these three issues in a unified framework. Specifically, the techniques, including sparse principal component analysis and subsampling, are employed to reduce the feature dimension. Then, a singular value decomposition (SVD)-based kernel is proposed to preserve the spatial information of the tactile sensing elements. The sample misalignment issue is addressed via the employment of a global alignment kernel. Moreover, the merits of these two kernels are fused through an ideal regularized composite kernel, which simultaneously takes the label information of the training set into consideration. The effectiveness of the proposed kernel-based approach is verified on a public touch modality data set with a comprehensive comparison with the competing methods.

Sensor drift is one of the severe issues that gas sensors suffer from. To alleviate the sensor drift problem, a gas sensor drift compensation approach is proposed based on local discriminant subspace projection (LDSP). The proposed approach aims to find a subspace to reduce the distribution difference between two domains, i.e., the source and target domain. Similar to domain regularized component analysis (DRCA) which is a recently proposed sensor drift correction method, the mean distribution discrepancy is minimized in the common subspace in our approach. LDSP extends DRCA in two aspects, i.e., it not only takes the label information of the source data into consideration to reduce the possibility of the case that samples in the subspace with different class labels stay close to each other, but also borrows the idea of locality-preserving projection to deal with multimodal data. Specifically, inspired by local Fisher discriminant analysis (LFDA), the label information is utilized to maximize the local between-class variance of source data in the latent common subspace and simultaneously minimize the local within-class variance. The formulation of LDSP is a generalized eigenvalue problem that can be readily solved. The experimental results have shown the proposed method outperforms other gas sensor drift compensation methods in terms of classification accuracy on two public gas sensor drift datasets.

Tactile sensing with spiking neural networks (SNNs) has attracted increasing attention in the past decades. In this article, a novel SNN framework is proposed for the tactile surface roughness categorization task. In contrast to supervised SNN methods such as ReSuMe and Tempotron that require prespecifying target spike trains, the presented method performs the classification through directly comparing the distance between multineuron spike trains. Unlike simple spike train fusion methods using average pairwise spike train distance or pooled spike train distance, the proposed method merges spike trains from different neurons with the multineuron spike train distance, which can capture the complex correlation of multiple spike trains. Specifically, the spike trains are generated via the Izhikevich neurons from tactile signals. The similarity of the multineuron spike trains is computed using the multineuron Victor–Purpura spike train distance, which can be efficiently implemented in an inductive manner. The classification can be performed by incorporating $k$ -nearest neighbors and the multineuron spike train distance as a similarity metric. The proposed framework is quite general, i.e., other multineuron spike train distances and spike train kernel-based methods can be readily incorporated. The effectiveness of the proposed method has been demonstrated on a tactile data set by comparing it with various feature- and spike-based methods. Note to Practitioners —In the soft neuromorphic implementation of biomimetic tactile sensing and the development of the tactile sensing capability in neurobotic systems, the processing and analysis of spike-like tactile signals are quite common. Inspired by human tactile perception, this article proposes a novel supervised spiking neural network method for tactile sensing tasks. The traditional methods have to prespecify target spike trains, which is still an open question. In addition, the current ways to fuse spike trains from multiple neurons are far from mature. This article tackles these two problems using spike train similarity comparison with multineuron spike train distance. The direct spike train similarity comparison avoids the need to prespecify target spike trains. The multineuron spike train distance can inherently fuse spike trains from different neurons. It is demonstrated that the proposed method is able to effectively perform classification in a tactile roughness discrimination task.

High flexible and high precise manipulation is one of the most critical technique for complex microsystem’s measurement, manufacture, and assembly. Although recent advances in microrobotics have successfully realized the automatic manipulation and positioning of tiny objects, their flexible manipulation in 3-D free space remains a challenge, such as the wide-angle rotation manipulation of microsize sphere, due to the complicate surface forces. Herein, this article proposed a feedforward model and realized the precise rotation for microsized sphere by two cooperative flexible micropipettes. Firstly, a microrobotic manipulation system with six degrees-of-freedom (DOFs) was developed and integrated with the microscope. Then, a feedforward compensation control strategy involving dual rotation was proposed for the precise manipulation of microsized sphere ( ${\sim }90~\mu \text {m}$ ) based on the analysis of contact forces. As a result, the rotation of the microsized sphere in two different planes was realized and the microsized sphere release procedure was also accomplished after rotation. Compared with existing techniques only allowing limited amplitudes rotation, this article realizes wide-angle rotation manipulation of microsized sphere in 3-D free space. This research opens new prospects for the microsized object accurate manipulation, which is expected to give a long-term impact for complex microsystem’s manufacture and assembly. Note to Practitioners —This article is motivated by the problem of the flexible manipulation of tiny object in 3-D space. The proposed nanorobotic manipulation system, two micropipettes and feedforward compensation model control strategy could realize the precise translational and rotational manipulation of microbeads in 3-D space, which offers obvious advantages of existing techniques. The proposed system and method could be a general solution for precise and flexible micromanipulation. Thus, it could find wide applications ranging from fundamental research to industrial applications, such as biological cell positioning, characterization of a particular micro/nanoregion, microassembly, and manufacturing.

Viewing a particular region of an object with high magnification is essentially significant in both fundamental research and practical applications, however, imaging the sample from multi-direction is still very challenging for current microscopes. Herein, a centered miniature rotation robot under microscopy is presented to achieve multi-directional imaging of small objects. First, the miniature rotatable robot is constructed by three nano motors, including one rotational and two linear-motors. Then, a new centering strategy of neighborhood-window difference is proposed to center the micro/nano sample onto the rotation axis of the robot (error less than 20 pixels). In experiments, the sample is tested to be imaged from multi-direction (360°) by rotating the robot continuously. To highlight the versatility of this system, we demonstrate the multi-directional imaging under both optical microscope (OM) and scanning electron microscope (SEM). Compared with current techniques, this system is able to provide richer morphology information at small scale benefiting from the multi-directional imaging, which is expected to have long-term impacts on microscopy observation, micro defect inspection, nanomaterial characterization and other related fields.

Robotic soldering has emerged as one of the key technologies for the through-hole technology (THT) assembly process. However, the determination of the soldering process parameters in those systems are still depends on engineer's experience. In this paper, we propose an automatic vision-based soldering process parameters determination method based on a mathematical model of standard solder joint. The system framework of this method and the detailed process of solder feeding volume calculation are also given. The proposed method is verified by experiments and compared with the soldering process parameters in published literature. Finally, we also analyze that the average computing time of the proposed method for new solder joints is 31.39ms, which is much faster than previous manual debugging method.

The number of stroke patients is rapidly increasing in the elderly society, which leads to growing demand for lower limb rehabilitation training. Currently, one patient needs two or more therapists for assistance during gait training. It results in the shortage of therapists ' population, furthermore, heavy works load on the therapist. The emerging robotic technologies provide a solution to assist the therapist, and a number of corresponding researches have been reported. However, most of the existing rehabilitation robots adopt single-arm or double-arm structure, which pays less attention on motor coordination training for the stroke patients. Here, a four-arm rehabilitation robot (FARR) is proposed to assist the hemiplegic patient for motor coordination training. First, the rehabilitation demand is analysed and the corresponding robot mechanism is designed. Then, the kinematics of the robot based on the D-H expression is constructed, and the workspace is obtained. Thirdly, the speed control strategy and the cooperative control for gait training are constructed. The experiment of speed response verifies the superior tracking performance of the robotic joints, and the experiment of using the robot for gait training by a simulated subject is performed. These results prove the feasibility of the designed robot.

In the context of robotic high-precision soldering, we propose an image-based pin alignment control method based on active plastic deformation. The plastic deformation is a well-known failure mechanism in most situations, which includes a phenomenon that the objects do not return original state. Here, in contrast to this convention, we utilize the plastic deformation of the metal pin to do pin alignment for improving the quality of the solder joint. To address this, we embed the springback compensation into the image-based pin alignment controller. Lastly, the proposed strategy is successfully demonstrated and evaluated in a practical modified robotic manipulation system. The result shows that the alignment error is less than 20µm, which is far less than pin alignment without considering plastic deformation and elastic recovery. This work considers active plastic deformation and spontaneous elastic recovery of soft object, which would greatly promote the use of robotics in micromanufacturing and microfabrication in lad and industry, especially for soft objects.

The functionalization of microrobots is essential for realizing their biomedical application in targeted cargo delivery, but the multifunctional integration of microrobots and controllable cargo delivery remains an enormous challenge at present. This work reports a kind of multi-functionalized micro-helical robot with superior loading capabilities for the controlled release of encapsulants. The magnetic microrobot, with a multilayer capsule helical structure, was developed via multifunctional strategies, including microfluidic synthesis, polyelectrolyte complexation, and surface coating with magnetic nanoparticles. The microrobot is constructed of a helical structure from a calcium alginate microfiber via a co-axial capillary microfluidic system. Then, it is coated with a polyelectrolyte complexation membrane and decorated with magnetic nanoparticles. After multi-step layer-by-layer (LbL) assembly with functionalized units, the structure is converted to a helical capsule possessing a soft and biocompatible polysaccharide alginate/chitosan/alginate shell with Fe3O4 nanoparticles decorated on the surface. The functionalized microrobot not only enables wireless steering with rotational locomotion under the control of a six degrees of freedoms (6-DOFs) electromagnetic system at different frequencies, but it also possesses stimuli-responsive abilities owing to the semi-permeable membrane, which can trigger the controllable release of encapsulants in response to ions in the environment. This work provides an efficient strategy for the superior multi-functionalization of microrobots to achieve enhanced locomotion and encapsulation performance for the loading, transport, and targeted delivery of cargo.