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Abstract As a stress hormone existing in the human body, cortisol can reflect the psychological stress and health status in daily life, and is a potential biomarker of the body’s stress response. To effectively collect sweat and accurately identify the target, this paper reports a flexible wearable cortisol detection device with outstanding reliability and sensitivity. Molecular imprinted polymer (MIP) ensures cortisol specificity. And carbon nanotubes (CNT) on electrodes increase sensitivity, expanding the detection range to 10−3 to 104 nM, with sensitivity at 189.2 nA/lg(nM). In addition, porous chitosan hydrogel (PCSH) collects sweat effectively, its adhesive properties and 80% swelling rate offer a low-cost alternative to microfluidics. Flexible printed circuit board (FPCB) and serpentine electrode (SE) ensure device durability. This non-invasive, highly sensitive device offers a novel method for mental stress monitoring and clinical diagnosis, advancing human physiological state monitoring.

Abstract The structure and mechanism of the human visual system contain rich treasures, and surprising effects can be achieved by simulating the human visual system. In this article, starting from the human visual system, we compare and discuss the discrepancies between the human visual system and traditional machine vision systems. Given the wide variety and large volume of visual information, the use of non-von Neumann structured, flexible neuromorphic vision sensors can effectively compensate for the limitations of traditional machine vision systems based on the von Neumann architecture. Firstly, this article addresses the emulation of retinal functionality and provides an overview of the principles and circuit implementation methods of non-von Neumann computing architectures. Secondly, in terms of mimicking the retinal surface structure, this article introduces the fabrication approach for flexible sensor arrays. Finally, this article analyzes the challenges currently faced by non-von Neumann flexible neuromorphic vision sensors and offers a perspective on their future development.

Abstract In this paper, the prescribed tracking performance control problem is addressed for uncertain nonlinear systems with unknown periodically time‐varying parameters and arbitrary switching signal. By utilizing radial basis function neural network and fourier series expansion, an approximator is developed to overcome the difficulty of identifying unknown periodically time‐varying and nonlinearly parameterized functions. To achieve the ideal tracking control performance and eliminate the influence of filtering error, a novel command filter‐based adaptive neural network prescribed tracking performance controller is designed by introducing a filtering compensation mechanism. Differently from the standard Backstepping technique, the proposed control scheme eliminates the “explosion of complexity” problem and relaxes the constraint condition on the reference signal. And then, it is warranted that the closed‐loop system is semi‐globally ultimately uniformly bounded and the tracking error is always limited to the specified region bounded by the performance functions. Three simulation examples are used to demonstrate the feasibility of the developed technique in this paper.

Abstract The sole situation of semi-crystalline structure induced single performance remarkably limits the green cryogels in the application of soft devices due to uncontrolled freezing field. Here, a facile strategy for achieving multifunctionality of cryogels is proposed using total amorphization of polymer. Through precisely lowering the freezing point of precursor solutions with an antifreezing salt, the suppressed growth of ice is achieved, creating an unusually weak and homogenous aggregation of polymer chains upon freezing, thereby realizing the tunable amorphization of polymer and the coexistence of free and hydrogen bonding hydroxyl groups. Such multi-scale microstructures trigger the integrated properties of tissue-like ultrasoftness (Young’s modulus

Abstract This paper proposes a state estimation approach ‘robust strong tracking unscented Kalman filter with unknown inputs’ that can be applied to non‐linear systems with unknown inputs. Specifically, the non‐linear state and measurement equations are linearised by statistical linearisation. Then, the estimation equation of the unknown input is derived based on the weighted least squares method. The multiple suboptimal fading factor is introduced into a priori error covariance matrix to improve the tracking ability for the inaccuracy of the system model and the abrupt change of state variables caused by unknown inputs. Finally, based on the unbiased minimum variance estimation, the unbiased state estimation and the error covariance matrix are derived. Singular value decomposition is performed on the error covariance matrix to improve the stability of the algorithm. Simulated results validate the effectiveness of the proposed method.

Abstract This paper addresses constrained robust control for dynamic positioning of the Liquefied Natural Gas (LNG) Carrier in side‐by‐side offloading operations subject to input time delay. During the LNG offloading operation, the relative motions of the multibody system are connected by the LNG offloading arm. To ensure the operation safety, robust control with asymmetric Barrier Lyapunov Function is proposed considering predefined output constraints under the unknown dynamics and external disturbances. In offshore engineering, due to the mechanical effects, the thrusters of the LNG carrier in the presence of time‐delay should be investigated to guide the design practice. The predictor‐based method is employed to deal with the time delay induced by the control inputs. It is proven that the proposed scheme can guarantee semi‐global uniform ultimate boundedness. The performance and feasibility of the proposed control are further verified by simulation studies.

Wall bulge maintenance and repairing is an essential task for autonomous decoration robots. The problem of the wall bulge endpoints regression refers to identifying the position of the wall bulge endpoints in spatial coordinates. This problem is of significant importance for autonomous decoration robots as these robots target automatic maintenance and repairing of wall bulges, they must automatically recognize where to start and stop the repairing process. Training deep convolutional neural networks for supervised computer vision tasks requires a large number of annotated images. Since gathering annotated images for this task is difficult, laborious, and time-consuming, we proposed a model for detecting the wall bulge endpoints position based on deep transfer learning. Our proposed model is capable of classifying the wall bulge into one of four classes according to its orientation. Deep transfer learning transfers the knowledge acquired by deep learning models trained for a specific task and domain to another different but related task and domain. Our proposed model is mainly based on deep convolutional neural networks pre-trained on large datasets for tasks of object classification and detection. We transfer the knowledge acquired by the model from these tasks to solve both problems in our new task.

Abstract As an extension of the conventional set‐point control problem, the dynamic region‐based tracking control scheme with obstacle avoidance is proposed for a class of uncertain strict‐feedback non‐linear systems. A novel adaptive tracking controller is designed by a fusion of artificial potential field, recursive backstepping approach, neural networks, dynamic surface control technique, calculus method, and Lyapunov stability theory. In the proposed control scheme, the objective region cannot be required to have a regular shape or a fixed size for the passibility of the system in constrained space. The region tracking error is transformed into a new virtual error variable for recursively designing a dynamic surface controller, and the dimension of neural network inputs can be greatly reduced, especially for high‐order systems. The Lyapunov theorem is used to confirm the stability and uniform boundedness of all closed‐loop signals. Simulation results are provided to demonstrate the effectiveness of the proposed controller.

Visual navigation (vNavigation) is a key and fundamental technology for artificial agents' interaction with the environment to achieve advanced behaviors. Visual navigation for artificial agents with deep reinforcement learning (DRL) is a new research hotspot in artificial intelligence and robotics that incorporates the decision making of DRL into visual navigation. Visual navigation via DRL, an end-to-end method, directly receives the high-dimensional images and generates an optimal navigation policy. In this paper, we first present an overview on reinforcement learning (RL), deep learning (DL) and deep reinforcement learning (DRL). Then, we systematically describe five main categories of visual DRL navigation: direct DRL vNavigation, hierarchical DRL vNavigation, multi-task DRL vNavigation, memory-inference DRL vNavigation and vision-language DRL vNavigation. These visual DRL navigation algorithms are reviewed in detail. Finally, we discuss the challenges and some possible opportunities to visual DRL navigation for artificial agents.

Vibration has a great influence on the working accuracy of flexible-link manipulators. In this paper, an online trajectory planning method for the flexible-link manipulator is proposed to suppress the vibration. Firstly, the vibration dynamic model of planar flexible-link manipulator is established, and the actual vibration trajectory of manipulator is solved by reasonable simplification. Then, taking residual energy of vibration as an objective function, the optimizer based on Particle Swarm Optimization(PSO) intelligent search algorithm is employed to search the motion trajectory of the manipulator with the best vibration suppression effect. Finally, the optimizer based on back propagation neural networks(BPNN) is employed instead of PSO optimizer to generate manipulator motion trajectory rapidly, and the optimal trajectory database searched by PSO optimizer is used to train BPNN. Simulation results show that this method can generate the trajectory rapidly and suppress the vibration effectively, and can be applied to the online trajectory planning of flexible manipulator.

Motion segmentation is one of the fundamental steps for detection, tracking, and recognition, and it can separate moving objects from the background. In this paper, we propose a spatial-motion-segmentation algorithm by fusing the events-dimensionality-preprocessing algorithm (EDPA) and the volume of warped events (VWE). The EDPA consists of depth estimation, linear interpolation, and coordinate normalization to obtain an extra dimension (Z) of events. The VWE is conducted by accumulating the warped events (i.e., motion compensation), and the iterative-clustering algorithm is introduced to maximize the contrast (i.e., variance) in the VWE. We established our datasets by utilizing the event-camera simulator (ESIM), which can simulate high-frame-rate videos that are decomposed into frames to generate a large amount of reliable events data. Exterior and interior scenes were segmented in the first part of the experiments. We present the sparrow search algorithm-based gradient ascent (SSA-Gradient Ascent). The SSA-Gradient Ascent, gradient ascent, and particle swarm optimization (PSO) were evaluated in the second part. In Motion Flow 1, the SSA-Gradient Ascent was 0.402% higher than the basic variance value, and 52.941% faster than the basic convergence rate. In Motion Flow 2, the SSA-Gradient Ascent still performed better than the others. The experimental results validate the feasibility of the proposed algorithm.

This study investigates the observer-based sliding mode load frequency control for multiarea interconnected power systems under deception attack. By introducing the observer and combining it with the system state equation, the expression of the system error is obtained. A sliding mode surface is proposed to make sure the state of the systems to be stable. Then, the state equation of the system under sliding mode control is derived. The asymptotic stability of the whole system is proved by using the linear matrix inequality (LMI) technique and Lyapunov stability theory. Furthermore, a sliding mode control law is proposed to ensure that the attacked power system can reach a stable position. Numerical simulation results are presented to support the correctness of the results.

This paper investigates the problem of asynchronous repetitive control for a class of discrete-time Markovian switching systems. The control goal is to track a given periodic reference without steady-state error. To achieve this goal, an asynchronous repetitive controller that renders the overall closed-loop switched system mean square stable is proposed. To reflect realistic scenarios, the proposed approach does not assume that the system modes are available synchronously to the controller but instead designs a detector that provides estimated values of the system modes to the controller. Based on a detected-mode-dependent estimator, the plant and asynchronous repetitive controller are formulated as a closed-loop stochastic system. By utilizing tools from stochastic Lyapunov–Krasovskii stability theory, we develop sufficient conditions in terms of linear matrix inequalities (LMIs) such that the closed-loop system is mean square stable and also simultaneously establish a synthesis procedure for obtaining the gain matrices. We provide numerical simulations on an electrical circuit switched system to illustrate the approach.

Wall defect detection is an important function for autonomous decoration robots. Object detection methods based on deep neural networks require a large number of images with the handcrafted bounding box for training. Nonetheless, building large datasets manually is impractical, which is time-consuming and labor-intensive. In this work, we solve this issue to propose the low-shot wall defect detection algorithm using deep reinforcement learning (DRL) for autonomous decoration robots. Our algorithm first utilizes the attention proposal network (APN) to generate attention regions and applies AlexNet to extract the features of attention patches to further reduce computation. Finally, we train our method with deep reinforcement learning to learn the optimal detection policy. The experiments are implemented on a low-shot dataset in which images are collected from real decoration environments, and the experimental results show the proposed method can achieve fast convergence and learn the optimal detection policy for wall defect images.

Complex marine environment has an adverse effect on the object detection algorithm based on the vision sensor for the smart ship sailing at sea. In order to eliminate the motion blur in the images during the navigation of the smart ship and ensure safety, we propose SharpGAN, a new image deblurring method based on the generative adversarial network (GAN). First of all, we introduce the receptive field block net (RFBNet) to the deblurring network to enhance the network’s ability to extract blurred image features. Secondly, we propose a feature loss that combines different levels of image features to guide the network to perform higher-quality deblurring and improve the feature similarity between the restored images and the sharp images. Besides, we use the lightweight RFB-s module to significantly improve the real-time performance of the deblurring network. Compared with the existing deblurring methods, the proposed method not only has better deblurring performance in subjective visual effects and objective evaluation criteria, but also has higher deblurring efficiency. Finally, the experimental results reveal that the SharpGAN has a high correlation with the deblurring methods based on the physical model.

This study presents a new perspective for autonomous mobile robots path searching by proposing a biasing direction towards causal entropy maximisation during random tree generation. Maximum entropy-biased rapidly-exploring random tree (ME-RRT) is proposed where the searching direction is computed from random path sampling and path integral approximation, and the direction is incorporated into the existing rapidly-exploring random tree (RRT) planner. Properties of ME-RRT including degenerating conditions and additional time complexity are also discussed. The performance of the proposed approach is studied, and the results are compared with conventional RRT/RRT* and goal-biased approach in 2D/3D scenarios. Simulations show that trees are generated efficiently with fewer iteration numbers, and the success rate within limited iterations has been greatly improved in complex environments.

This paper studies the velocity free platoon formation control for unmanned surface vehicles (USVs) with the model uncertainties and output constraints. Firstly, a reconstruction module is designed to estimate the velocity of the leader, which will be completed in finite time and will reduce the communication burden. Along with this, the model-based control combined with the symmetric barrier Lyapunov functions (BLF) method is designed to guarantee the output constraints. Then, the model uncertainties of the USV are approximated by the neural networks (NNs) and the NN BLF control is developed. To achieve the desired formation pattern, the constraints, including collision avoidance and communication distance, are under consideration. Finally, we proved that our system is semiglobally uniformly ultimately bounded (SGUUB) and verified the effectiveness of this approach by simulations.

In this study, the authors present the role playing learning scheme for a mobile robot to navigate socially with its human companion in populated environments. Neural networks (NNs) are constructed to parameterise a stochastic policy that directly maps sensory data collected by the robot to its velocity outputs, while respecting a set of social norms. An efficient simulative learning environment is built with maps and pedestrians trajectories collected from a number of real-world crowd data sets. In each learning iteration, a robot equipped with the NN policy is created virtually in the learning environment to play itself as a companied pedestrian and navigate towards a goal in a socially concomitant manner. Thus, this process is called role playing learning, which is formulated under a reinforcement learning framework. The NN policy is optimised end-to-end using trust region policy optimisation, with consideration of the imperfectness of robot's sensor measurements. Simulative and experimental results are provided to demonstrate the efficacy and superiority of the proposed method.

For positioning a moving ultrasonic transmitter, most of the existing ultrasonic positioning systems require the use of a bank of correlators to estimate the Doppler shift associated with its movement which require high computational complexity. In this paper, for positioning a moving transmitter, a computationally efficient a Doppler shift estimation and compensation technique is proposed. As the proposed approach has the ability to measure the Doppler shift directly from the received signal, it does not require to use a bank of correlators to estimate the Doppler shift associated with its movement of the transmitter.

One of the fundamental requirements for an artificial hand to successfully grasp and manipulate an object is to be able to distinguish different objects’ shapes and, more specifically, the objects’ surface curvatures. In this study, we investigate the possibility of enhancing the curvature detection of embedded tactile sensors by proposing a ridged fingertip structure, simulating human fingerprints. In addition, a curvature detection approach based on machine learning methods is proposed to provide the embedded sensors with the ability to discriminate the surface curvature of different objects. For this purpose, a set of experiments were carried out to collect tactile signals from a 2 × 2 tactile sensor array, then the signals were processed and used for learning algorithms. To achieve the best possible performance for our machine learning approach, three different learning algorithms of Naïve Bayes (NB), Artificial Neural Networks (ANN), and Support Vector Machines (SVM) were implemented and compared for various parameters. Finally, the most accurate method was selected to evaluate the proposed skin structure in recognition of three different curvatures. The results showed an accuracy rate of 97.5% in surface curvature discrimination.

This paper presents a chirp based ultrasonic positioning system (UPS) using orthogonal chirp waveforms. In the proposed method, multiple transmitters can simultaneously transmit chirp signals, as a result, it can efficiently utilize the entire available frequency spectrum. The fundamental idea behind the proposed multiple access scheme is to utilize the oversampling methodology of orthogonal frequency-division multiplexing (OFDM) modulation and orthogonality of the discrete frequency components of a chirp waveform. In addition, the proposed orthogonal chirp waveforms also have all the advantages of a classical chirp waveform. Firstly, the performance of the waveforms is investigated through correlation analysis and then, in an indoor environment, evaluated through simulations and experiments for ultrasonic (US) positioning. For an operational range of approximately 1000 mm, the positioning root-mean-square-errors (RMSEs) &90% error were 4.54 mm and 6.68 mm respectively.