Cognitive state detection holds significant research value in the field of human–computer interaction and neural engineering. However, existing works are insufficient in modeling the temporal dynamics of multimodal physiological signals, which leads to heterogeneous distribution differences in cross-modal feature interactions. In addition, domain shift issues under cross-subject and few-sample conditions restrict the model generalization performance. To cope with these problems, this work proposes a cognitive state detection framework that integrates Transformer-based multimodal feature interaction and self-training of pseudolabel optimization. First, the multihead attention mechanism is introduced to model the temporal evolution patterns across modalities, dynamically harmonizing cross-modal contributions to extract cognitive state-related shared features. Then, a dual-model cross-validation strategy is designed to filter high-quality pseudolabeled samples from the target domain for subsequent self-training, effectively avoiding the dependency on auxiliary modules in domain adaptation. Finally, Extensive experiments show that the proposed work significantly improves the recognition accuracy, and the designed pseudolabel optimization mechanism can be transferred to related tasks without increasing model complexity.

This study investigates the trajectory tracking strategy of a snake robot with sideslip disturbance and unknown model parameters. To guide the robot to track the ideal trajectory faster and more accurately, an adaptive anti-sideslip strategy for a snake robot with the Integral Line-of-Sight (ILOS) function is reported. This technique eliminates direction sideslip and error fluctuation by using auxiliary integral terms and shortens the convergence time of state variables. Following the position and angle control objectives, the proposed controller considers the negative effects caused by the uncertainty and time variability of environmental parameters and compensates for the joint input using the adaptive update laws. The environment adaptability and tracking efficiency are improved. The stability analysis indicates that the state errors converge to the origin. The simulation and experiment data verifies the effectiveness and strength of the work.Note to Practitioners—This article was motivated by the problem of robust trajectory tracking for a snake robot in an environment with sideslip disturbance and unknown model parameters. In this environment, information of the motion space (for example, the coefficient of ground friction) cannot be obtained. In addition, there may be other system limitations (for example, motion sideslip limitations) and other operational limitations. These limitations are caused by the requirements of various common trajectory tracking objectives. These cases should also be considered in the control strategy. However, based on the existing methods of tracking control for snake robots, there is still a lack of a complete and reliable autonomous control scheme that can consider the above problems. On this basis, we present a reliable control strategy, which considers the above problems and the dynamic uncertainty of the model. In the future, we will extend the proposed method to the field of formation tracking control for multiple robots.

Given a set of calibrated images, Multiple View Stereo (MVS) applies end-to-end depth inference network to recover scene structure. However, previous methods designed pixel-visibility modules to aggregate cross-view cost, ignoring the consistency assumption of 2D contextual features in the 3D depth direction. The current multi-stage depth inference model also relies on intensive depth samples, which requires high memory consumption. To alleviate these problems, this work exploits edge-assisted epipolar Transformer for multi-view depth inference. The improvements of this work are summarized as follows: 1) The epipolar Transformer block is developed for reliable cross-view cost aggregation, and the edge detection branch is designed to constrain the consistency of epipolar geometry and edge features. 2) The dynamic depth range sampling mechanism based on probability volume is adopted to improve the accuracy of uncertain areas. Comprehensive comparisons with the state-of-the-art works indicate that our work can reconstruct dense scene representations with limited memory bottleblockNote to Practitioners—Learning-based MVS can obtain dense point clouds with accurate depth map estimation, which are widely applied in the fields of unmanned driving, battlefield environment perception and robot navigation. MVS-based scene reconstruction technology is the premise of the subsequent planning, decision-making and control of the human-machine system. To obtain dense scene representation with limited memory and runtime, this work proposes a multi-view stereo network with edge-assisted epipolar Transformer. Experiments on public benchmarks verify the feasibility and effectiveness of our model, which has good potential in battlefield environment reconstruction and human-computer interaction fields, and can provide intuitive and dense scene representation for decision-making assistance.

Extensive medical cases have substantiated the imperative need for researching rapid, precise, and convenient soft robots to assist traditional surgical methods and ensure the safety of emergency medical care required to save the lives of patients, particularly in scenarios involving unexpected mutations within the body. Hence, this paper presents the design of an emergency medical assistance robot based on biocompatible magnetic chitosan hydrogels and magnetic actuation. Firstly, a description of the designed magnetic chitosan hydrogel robot was demonstrated. Subsequently, a mechanistic analysis of the magnetic response characteristics of the magnetic chitosan hydrogel robot was conducted. The COMSOL modeling and simulation results demonstrated the feasibility of employing a rectangular permanent magnet with a gradient magnetic field distribution for robot actuation. Furthermore, precise and controllable deformation behaviors of the designed robot could be achieved by regulating the strength of the driving magnetic field, the magnetic properties of the robot, and the distance between the robot and the permanent magnet. Moreover, simulation results validated the capability of the robots to execute tapping motion, biomimetic flight, and thrombus removal through advanced structural design. The accomplishments of this study provide foundational support for the development of magnetically actuated devices in emergency medical scenarios and offer valuable insights into the deformation motion processes of magnetic actuation.Note to Practitioners—The motivation of the article is to develop a biocompatible magnetic chitosan hydrogel robot and investigate its precise control methods for emergency medical applications. In emergency medical situations, there is a great demand for fast and accurate medical interventions that are simple and easy to operate. Therefore, a magnetic chitosan hydrogel robot is designed in the article, and its magnetic driving force characteristics are studied through experiments and simulations. The biocompatible chitosan hydrogel robot possessed excellent magnetic responsiveness and remarkable controllable deformation behaviors under different magnetic field driving conditions, as proved by COMSOL simulations. Furthermore, the effects of driving magnetic field strength, the magnetic properties of the robots, and the distance between the robot and the permanent magnet on the magnetic-driven deformation behaviors were investigated, which was of significant importance in guiding the control of robot deformation. Besides, various motions could be achieved through the structure design of robots. Consequently, the magnetic chitosan hydrogel robot possesses the characteristics of convenience, speed, and precision through magnetic actuation, making it highly suitable for emergency medical applications.

Cancer starkly jeopardizes the security of human life as an intractable challenge in the realm of emergency medical care. Existing therapeutic modalities have thus far proven inadequate in effecting a complete eradication of cancer. This study introduces a microrobot-assisted approach to cancer treatment through the conceptualization of a magnetothermal biocompatible chitosan microrobot. The designed biocompatible chitosan microrobot possessed excellent thermal stability and magnetothermal performance. Leveraging computational modeling and simulation within the COMSOL framework, it was ascertained that the microrobot possessed a capacity for precision-controlled temperature elevation through the augmentation of coil current intensity and magnetic field frequency. Furthermore, the applicability of the microrobot for cancer therapy was confirmed through the establishment of a liver cancer model, which proved the microrobot’s adeptness in swiftly affecting temperature elevation and effectually ablating cancer tissue within a short time. The accomplishments of this study provide foundational support for the landscape of cancer treatment in emergency medical scenarios, thereby offering valuable insights into the magnetothermal cancer treatment process of microrobots.Note to Practitioners—The motivation of this paper is to develop a magnetothermal biocompatible chitosan microrobot and investigate its precise magnetothermal control properties for application in emergency medical scenarios of cancer treatment. Magnetothermal therapy avoids the damage to normal tissues caused by the use of chemical agents in cancer treatment, and its precise temperature control is crucial for the therapeutic effect. Therefore, a magnetothermal microrobot was designed based on biocompatible chitosan material and its magnetothermal controllable performance was studied by simulation. The magnetothermal chitosan microrobot possessed good thermal properties, and the temperature rise behaviors of the microrobot could be effectively controlled by changing the current intensity and magnetic field frequency. Furthermore, the ability of the magnetothermal microrobot to rapidly and efficiently increase the temperature to kill cancer tissues was verified in a liver cancer model. Therefore, the magnetothermal chitosan microrobot has a rapid, precise, and controllable temperature rise effect and is suitable for emergency medical applications in cancer treatment.

The industrial knowledge graph (IKG) can improve the cognitive intelligence of the manufacturing system and is recognized as one of the cores of the next-generation industrial management information system. Due to the multisource heterogeneous nature of industrial data, aligning entities with the same semantics (entity alignment) is the core technology for building large-scale, high-coverage IKGs. Existing approaches show that embedded learning of IKGs performs well for this task. However, most advanced methods ignore concept information when learning topological information about IKGs. Inspired by the ontology matching theory, in this article, we realize the importance of entity concepts in alignment. The conceptual semantics of entities can usually be obtained through the is–a relation. However, the IKG is usually constructed by triples (entity, relation, entity) automatically extracted from a large text corpus. This will lead to entities in the IKG having problems such as lacking conceptual information, belonging to multiple concepts, or having different concept granularities. To solve the two problems of lacking conceptual information and different concept granularity, we propose the concept-aware entity alignment network (CAEA), aggregating bidirectional relations and attributes to get the entity concept semantics by a novel concept-aware graph attention mechanism. The excellent performance of the CAEA can better support the construction of large and complete IKGs and support downstream applications such as industrial knowledge recommendation and assisted decision-making. To verify the performance of the CAEA on the IKG, we construct a new entity alignment benchmark using industrial control network security data and verify the effectiveness of the CAEA on the new benchmark and several mainstream datasets. Experimental results show that our method outperforms other state-of-the-art (SOTA) methods and promotes the development of IKGs.

Three-dimensional spiral gait enables a snake robot to climb over obstacles, cross caves, and adapt to complex environments. This article reports an antidisturbance path-following control method for a snake robot with a spiral gait. This method reduces the deviation of the robot's position in following the ideal path by estimating the time-varying parameters, the external disturbances, and the viscous friction coefficients. The estimations are used to compensate for the control inputs of the system, which can improve the adaptability of the robot to the environment. Then, the attitude and position errors can rapidly converge to the origin. An appropriate Lyapunov function is adopted to explore the stability of following errors. Experimental results show that the proposed method can accelerate the convergence rate of errors, reduce the fluctuation peak, and improve the following stability of snake robots.

Redundant robot manipulators play a significant role in modern industry. In this article, we propose a solution scheme to the trajectory tracking problem of the redundant robot manipulator with physical constraints through the Zhang neural dynamics method. Such problem is integrated into a time-varying system consisting of time-varying nonlinear equation (TVNE) and time-varying linear inequality (TVLI) and solved online by the varying-parameter Zhang neural dynamics (VPZND) model. It is ensured that the redundant robot manipulator can still perform the tracking task perfectly under the coexistence of time-varying bounded noise and physical constraints. Theoretical analysis proves that this VPZND model also has an explicit fixed convergence time. Numerical experiments confirm the feasibility of our VPZND model for TVLI. The trajectory tracking problem of the redundant robot manipulator with six or three degrees of freedom under the dual influence of physical constraints and noise is perfectly solved by the VPZND model, which is enough to verify its practical value.

To eliminate the influence of nonlinear state terms in the highly-coupled unmanned aerial vehicle (UAV) model and improve the aircraft’s ability to suppress wind field interferences, this work presents a path-following scheme for UAVs. This method uses the radial basis neural network (RBNN) to develop an adaptive approximation law for the gyroscopic effect function to balance for the influence of system uncertainty and nonlinear state terms on UAV modeling and reduce the dependence of the UAV’s roll and pitch control orders on attitude velocity information. In addition, the adaptive update laws of the disturbance predictions are designed to compensate for the control input and repress the chattering and deviation of the drone. The stability of the proposed controller was proven by using the Lyapunov theorem. Simulations and experiments have shown that the controller can perform faster convergence speed and higher following accuracy of the flight position and attitude errors.

This work reports an adaptive path-following controller for a multijoint snake robot (MSR) to improve the adaptability of the robot to the environment. The new strategy estimates the time-varying parameters of the system and the external interference to adjust the motion state of the robot in real time. Estimations are used to compensate for the joint torque of an MSR, thus reducing the fluctuation peak of path-following errors. In addition, this work designs an anti-sideslip line-of-sight (LOS) guidance strategy to avoid the deviation of the direction angle. The method can improve the tracking accuracy of an MSR, and the position errors enable the system to achieve uniformly ultimate boundedness (UUB). The angle errors converge to the origin to achieve stability. Experimental results demonstrate that the novel method can accurately estimate the time-dependent parameters, sideslip, and interference, raise the convergent speed of errors, and reduce the fluctuation peak.