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Adding corn straw to the anaerobic fermentation system of coal can significantly increase the gas production of coal-to-biomethane. However, the changes in substrate and microbial metabolism accompanying this combined fermentation are rarely studied. The Scanning Electron Microscope (SEM) test was utilized to analyze the microbial adhesion characteristics of coal and corn straw, while the X-ray Photoelectron Spectroscopy (XPS) test examined the changes in surface elements of coal. The results indicated that the optimal ratios of lean coal, weakly caking coal, and coking coal to corn straw were 2∶1, 2∶1, and 3∶1, respectively, and the corresponding methane yields were 17.28 mL/g, 12.51 mL/g, and 14.88 mL/g. The promotional effect of co-fermentation of different rank coal and straw was observed in the order of lean coal > coking coal > weakly caking coal. Microbial identification revealed that Sphaerochaeta and Proteiniphilum were the dominant bacterial genera in the mixed fermentation system, comprising 35.95% and 24.36% of the population, respectively. Additionally, Methanosarcina, Methanobacterium, and Methanoculleus emerged as the dominant archaea, constituting 49.71%, 31.83%, and 9.87%, respectively. In comparison with coking coal and weakly caking coal, the bacterial and archaeal genera, along with their associated gene functions responsible for carbohydrate metabolism in the combined fermentation broth of lean coal, coking coal, and weakly caking coal, were found to be dominant. This observation aligns with the principles governing methane production. Notably, in mixed substrate fermentation, as coal rank increased, bacterial presence on the coal surface gradually declined, while a substantial bacterial population consistently adhered to the surface of corn straw, indicating the latter's sustained advantage in degradation. After co-fermentation, the relative content of C element on the surface of coking coal and weakly caking coal decreased more significantly than that of lean coal, while the total relative content of oxygen-containing organic carbon ( C—O, C=O and O—C=O ) increased significantly, indicating that co-fermentation can promote the growth and metabolism of bacteria, so that more organic carbon can be converted into biological methane. The research results identified the effect of co-fermentation of different rank coal and corn straw at the micro level, which has reference significance for revealing the internal mechanism of co-fermentation of coal and corn straw.

Abstract Background Observational studies have reported that gut microbiota composition is associated with metabolic syndrome. However, the causal effect of gut microbiota on metabolic syndrome has yet to be confirmed. Methods We performed a bidirectional Mendelian randomization study to investigate the causal effect between gut microbiota and metabolic syndrome in European population. Summary statistics of gut microbiota were from the largest available genome-wide association study meta-analysis (n = 13,266) conducted by the MiBioGen consortium. The summary statistics of outcome were obtained from the most comprehensive genome-wide association studies of metabolic syndrome (n = 291,107). The inverse-variance weighted method was applied as the primary method, and the robustness of the results was assessed by a series of sensitivity analyses. Results In the primary causal estimates, Actinobacteria (OR = 0.935, 95% CI = 0.878–0.996, P = 0.037), Bifidobacteriales (OR = 0.928, 95% CI = 0.868–0.992, P = 0.028), Bifidobacteriaceae (OR = 0.928, 95% CI = 0.868–0.992, P = 0.028), Desulfovibrio (OR = 0.920, 95% CI = 0.869–0.975, P = 0.005), and RuminococcaceaeUCG010 (OR = 0.882, 95% CI = 0.803–0.969, P = 0.009) may be associated with a lower risk of metabolic syndrome, while Lachnospiraceae (OR = 1.130, 95% CI = 1.016–1.257, P = 0.025), Veillonellaceae (OR = 1.055, 95% CI = 1.004–1.108, P = 0.034) and Olsenella (OR = 1.046, 95% CI = 1.009–1.085, P = 0.015) may be linked to a higher risk for metabolic syndrome. Reverse MR analysis demonstrated that abundance of RuminococcaceaeUCG010 (OR = 0.938, 95% CI = 0.886–0.994, P = 0.030) may be downregulated by metabolic syndrome. Sensitivity analyses indicated no heterogeneity or horizontal pleiotropy. Conclusions Our Mendelian randomization study provided causal relationship between specific gut microbiota and metabolic syndrome, which might provide new insights into the potential pathogenic mechanisms of gut microbiota in metabolic syndrome and the assignment of effective therapeutic strategies.

For patients with repeated stenosis of autologous arteriovenous fistula, percutaneous transluminal angioplasty (PTA) or bare metal stent placement had limited efficacy. Rapamycin was reported to inhibit neointimal hyperplasia and keep blood vessels patent. In this study, we reported a case with refractory stenosis, i.e., a short duration of patency maintenance after each repeated PTA, which was treated with a rapamycin-eluting stent (RES). The RES extended the patency duration from 4 to 5 months on average to 14 months. The stent was used to maintain dialysis for over 30 months. RES may be an effective way to treat refractory stenosis and salvage limited vascular resources.

Currently, divers face significant safety risks when cleaning marine organisms from the steel structures of offshore underwater platform jackets. Consequently, utilizing robots instead of divers to carry out underwater biofouling removal operations will be an important development direction for the underwater maintenance of offshore platforms in the future. In this study, a wall-climbing robot was designed to clean marine organisms from the underwater surface of a platform jacket leg. The overall structure of the underwater cleaning wall-climbing robot is introduced, including the cleaning actuator and the variable curvature-adapted connecting rod mechanism. The corresponding relationship between the variable curvature-adapted connecting rod mechanism and the jacket leg is analyzed in detail. The variable curvature-adapted connecting rod mechanism was optimized using a genetic algorithm to ensure that the underwater cleaning wall-climbing robot can adapt to a minimum diameter of 1 m for the jacket leg. By drawing on Airy wave theory and random wave theory, the Airy wave parameters for waves were analyzed under different sea conditions, considering practical application scenarios. By using Fluent software 2022, a 2D numerical wave tank was constructed to simulate waves under various sea conditions, and the wave surface shapes for different sea states were determined. By building on the Morison equation, a method for calculating the horizontal wave forces on the underwater cleaning wall-climbing robot using the equivalent area and equivalent volume is proposed. By using the two aforementioned methods, the horizontal wave forces on the underwater cleaning wall-climbing robot under specific sea states were determined. The horizontal wave forces of the underwater cleaning wall-climbing robot under different sea conditions were analyzed and simulated in a 3D numerical wave tank. By comparing the theoretical analysis results with the numerical simulation results, where the maximum difference at the extreme points is approximately 11%, the feasibility of the proposed horizontal wave force estimation method was verified.

Rope-driven robots are increasingly used to safely and efficiently clean complex glass curtain walls. However, continuous global cleaning is difficult for most robots because of their poor performance in overcoming obstacles and adapting to curved surfaces, and an inconvenient winch calibration on complex surfaces further burdens such work. This paper presents a 3-DOF rope-driven robot and a winch self-calibration method for efficient cleaning. The robot, by integrating a 5-rope parallel configuration, a self-adaptive cleaning body, and a self-compensating driving winch, is designed to perform continuous, compliant, and accurate spatial motion on curved walls with obstacles. By deducing the kinematic model, the constraint relationship related to orderly arranged winch positions, maneuverable body positions, and accessible rope lengths is established during the robot tracking 3D trajectory. Combining the established relationship, a series of regulations are formulated for easily acquiring body positions and rope lengths, and then a self-calibration method is proposed by accurately calculating winch positions without using professional instruments. Experimental results show that the robot can perform global and precise movement on complex glass surfaces. Applying the proposed method, the maximum winch calibration error is 6.6 mm, and the maximum body tracking error is controlled within 9.6 mm.

At present, numerous wall-climbing robots have been developed, and applied in ship manufacturing for weld detection to ensure safe navigation. Limited by rigid mechanical structure and complex detection, mostly existing robots are hardly to complete weld detection by using fluorescent magnetic particles. Based on permanent magnet adsorption, a wheeled wall-climbing robot is developed to realize the stable adsorption and flexible movement on ship wall. A detection mechanism is designed using a series and parallel flexible adaptation structure to keep cross yokes and detection area close for effective detection. A unified mechanical model is established by analyzing the angle between robot attitude and gravity, to solve safe adsorption and flexible movement for different detection conditions. Integrated the multisensor information and collaboration between control component, an automatic detection control workflow conforms to the standard process is proposed. Experiments show that the robot can move on curvature wall flexibly and stably, complete the weld detection with the standard process, and clearly display the shape and depth of the small defects (groove depth ≥ 30 μm) in standard specimen.

Understanding the final fate of nanomaterials (NMs) in the liver is crucial for their safer application. As a representative two-dimensional (2D) soft nanomaterial, graphene oxide (GO) has shown to have high potential for applications in the biomedical field, including in biosensing, drug delivery, tissue engineering, therapeutics, etc. GO has been shown to accumulate in the liver after entering the body, and thus, understanding the GO–liver interaction will facilitate the development of safer bio-applications. In this study, the hepatic clearance of two types of PEGylated GOs with different lateral sizes (s-GOs: ~70 nm and l-GOs: ~300 nm) was carefully investigated. We found that GO sheets across the hepatic sinusoidal endothelium, which then may be taken up by the hepatocytes via the Disse space. The hepatocytes may degrade GO into dot-like particles, which may be excreted via the hepatobiliary route. In combination with ICP-MS, LA-ICP-MS, and synchrotron radiation FTIR techniques, we found that more s-GO sheets in the liver were prone to be cleared via hepatobiliary excretion than l-GO sheets. A Raman imaging analysis of ID/IG ratios further indicated that both s-GO and l-GO generated more defects in the liver. The liver microsomes may contribute to GO biotransformation into O-containing functional groups, which plays an important role in GO degradation and excretion. In particular, more small-sized GO sheets in the liver were more likely to be cleared via hepatobiliary excretion than l-GO sheets, and a greater clearance of s-GO will mitigate their hepatotoxicity. These results provide a better understanding of the hepatic clearance of soft NMs, which is important in the safer-by-design of GO.

The objective of this research was to investigate the effect of bulking agents on the maturity and gaseous emissions of composting kitchen waste. The composing experiments were carried out by selected core bacterial agents and universal bacterial agents for 20 days. The results demonstrated that the addition of core microbial agents effectively controlled the emission of typical odor-producing compounds. The addition of core and universal bacterial agents drastically reduced NH3 emissions by 94% and 74%, and decreased H2S emissions by 78% and 27%. The application of core microbial agents during composting elevated the peak temperature to 65 °C and in terms of efficient temperature evolution (>55 °C for 8 consecutive days). The organic matter degradation decreased by 65% from the initial values for core microbial agents were added, while for the other treatments the reduction was slight. Adding core microbial agents to kitchen waste produced mature compost with a higher germination index (GI) 112%, while other treatments did not fully mature and had a GI of Weissella, Ignatzschineria, and Bacteroides. Network and redundancy analysis (RDA) revealed that the core microbial agents enhanced the relationship between bacteria and the eight indicators (p < 0.01), thereby improving the bio transformation of compounds during composting. Overall, these results suggest that the careful selection of appropriate inoculation microorganisms is crucial for improved biological transformation and nutrient content composting efficacy of kitchen waste.

The paper presents a wall-climbing robot featuring self-compliance for variable curvature façades. The high payload and maneuverability make it highly potential in heavy-duty operation of industrial applications. The robot consists of two traction modules and one link module. Each traction module is equipped with magnetic adhesion and crawler traction submodules. The two traction modules are connected by the link module with 4 degrees of freedom (DoF) of passive compliance. Variable curvature façade self-compliance is achieved by the passive compliant link module, which results in the attitude change decoupling of the two traction modules. The attitude can be adjusted under the effect of magnetic adhesion force to comply with the curvature variations. High payloads are achieved by the large contact area between the crawler and the surface. Omni-directional high maneuverability is enabled by the speed difference between the motors of two traction modules. The robot can maneuver in any direction on the surface with a minimum radius of 1 m and carry 36 kg payload on vertical surfaces.

At present, a large number of magnetic wall-climbing robots are applied to various magnetically conductive metal facades for detection and anti-corrosion work. Limited by the wall-climbing mechanism and adsorption device, most wall-climbing robots can only climb on smooth walls, and it is difficult to adapt to complex walls. Therefore, by studying the multi-media magnetic circuit conduction mechanism, a permanent magnetic adsorption wheel with a magnet array arrangement was designed in this study and applied to a hinge-type wall-climbing robot. By analyzing the influence of structural parameters on the adsorption performance and optimization design, a magnetic wheel structure with a symmetric structure that can meet a variety of adsorption requirements was obtained. To analyze the mechanical characteristics of the wall-climbing robot under complex facade conditions, we researched the adsorption performance of the designed magnetic wheel in different wall structures. Finally, the adhesion force of the magnetic wheel was verified through experimental measurements, and it was found that the hinged wall-climbing robot could adapt to different structural features and complete wall-transition and obstacle-crossing movements.

Rope-driven robots are increasingly being applied for the efficiently cleaning of glass curtain walls. However, increasingly complex wall surfaces and the various shapes of obstacles may block the robot and reduce coverage. In this study, three-DOF rope-driven cleaning robots and a full-coverage path-planning algorithm were developed to achieve global operation. The robot adopts a five-rope parallel configuration, and four winches are mounted on the wall and one on the ground to produce 3D motion performance. We used a grid method to build the wall model to mark obstacles, and then we decomposed it according to the wall curvature to better access cleaning subareas. To further increase the cleaning coverage rate, a full-coverage path-planning algorithm based on an improved priority heuristic was designed, which does not ignore the inset area of U-shaped obstacles. By introducing two sets of priority criteria to judge the forward direction, the robot can switch directions to cover a whole area when encountering U-shaped obstacles. Furthermore, by planning a return route requiring the least amount of time when entering a dead zone, an escape strategy was developed to prevent the robot from being unable to choose a direction. The experimental results show that the robot, after applying the proposed path-planning algorithm, could complete the global cleaning of complex glass walls with various obstacles.

This paper proposes a novel wheel−leg hybrid robot that can be applied on both flat and rugged terrains, it utilizes two passive transformable symmetrical wheels that combine the stability of the circular wheel and the obstacle climbing ability of the legged wheel. To minimize the number of actuators, the transformation process of the wheel is designed to be triggered passively when in contact with the obstacles. A new triggering mechanism is employed to eliminate the adverse effect of the robot’s weight on the transformation torque. The parameters of the wheel are optimized to maximize the climbing ability in low-friction conditions. The robot’s body length and angular velocity are also tuned based on the dynamic model during the obstacle climbing process. The simulation experiment results show that the robot can switch modes stably on terrain with a friction coefficient as low as 0.2, and can climb over an obstacle 3.9 times as tall as its wheel radius.

The positioning accuracy of a robot directly affects the quality of its operations. In this study, a calibration method is proposed based on combining a model with least-squares support vector regression (LSSVR) to improve robot positioning accuracy. First, a geometric error model of the robot is established. Second, singular value decomposition (SVD) and physical model analysis method are employed to process the coupling parameters in the error model to improve the accuracy and efficiency of identification. Third, as nongeometric errors hinder the construction of an accurate and complete mathematical model and affect the residual positioning errors of the robot, LSSVR is used to compensate for the residual positioning errors caused by nongeometric errors. The proposed method thus improves the accuracy and robustness of finite sample estimation. Finally, an experiment is performed on an IRB1410 robot with a parallelogram mechanism. The maximum/mean positioning errors of the robot decrease from 2.0348/1.0978 mm to 0.1659/0.0733 mm, and the effectiveness of the proposed method is verified. The proposed method has higher prediction accuracy and stability for small samples than other methods.

Remote center of motion (RCM) mechanisms, which are widely used in surgical robots, use mechanical constraints to realize movement around the RCM point. Errors in processing and assembly cause deviation of the RCM point and position error of the end-effector, these deviations cause the robot to exert excessive force on the incision during movement, leading to adverse effects that reduce the safety and accuracy of surgical robots based on the RCM. Based on kinematic calibration, this paper proposes a solution of forward and inverse kinematics considering the RCM point deviation, and this solution can simultaneously reduce the positioning error of the end-effector and the deviation of the RCM point. First, a calibration method based on the combination of the model and neural network is used to improve the kinematic accuracy of the robot. Second, a forward and inverse kinematics solution applied to RCM mechanisms is proposed, and this solution reduces the RCM point deviation during surgery. Finally, experiments were performed with an established surgery robot, verifying that the proposed method can simultaneously reduce the deviation of the RCM point and the position error of the end-effector. The proposed method is found to be superior to other methods and can improve the safety and accuracy of surgical robots.

A multistage adaptive lateral deformation tracked robot was designed to improve the passing ability of field robots. The designed robot can change its width under the combined action of space barrier constraint and internal stored elastic potential energy. The lateral sliding friction affected by grouser parameters during lateral deformation is a key factor that determines whether or not the robot can achieve deformation. This study focuses on the track terramechanics and grouser parameter optimization. On the basis of the theory of terramechanics, the interaction of the flexible track lateral movement with the ground model and traction model was established. Taking the requirement of traction as the constraint condition and the minimum lateral sliding resistance as the optimization objective, we used the multitarget optimization algorithm (NSGA-II) to optimize and analyze the grouser parameters. Then, the optimal combination of grouser parameters was obtained. Simulation analysis was carried out on RecurDyn software to complete the simulation verification of the lateral force and traction force under different grouser parameters. The correctness of the theoretical model and the optimization of the grouser parameters was verified through prototype experiments.

In recent years, many wall-climbing robots have been developed in the field of petrochemical storage tank maintenance. However, it is difficult for most of them to be widely used due to common problems such as poor adsorption capacity, poor adaptation to elevation, and low trajectory tracking accuracy. In order to solve the problem of the robot not being able to achieve high-precision operation on curved surfaces, a new wall-climbing robot system is designed. Based on the magnetic wheel adsorption method, a passive adaptive motion mechanism that can adapt to walls with different curvatures is proposed. In order to improve the trajectory tracking accuracy of the wall-climbing robot, the kinematic model of the wall-climbing robot is simplified, a velocity compensation controller is designed, and the stability of the controller is proved by introducing the Lyapunov equation. Through experiments, the controller designed in this paper is compared with the conventional controller to verify the effectiveness and superiority of the controller. The experimental results show that the robot can move safely and stably on curved surfaces, with improved tracking accuracy and reduced trajectory deviation caused by response time lag, and meets the maintenance operation requirements of wall-climbing robots.

In this research, we propose a novel wheel-legged mobile robot to address the problems of insufficient obstacle-crossing performance and poor motion flexibility of mobile robots in non-structural environments. Firstly, we designed the transformable wheel mechanism and tail adaptive mechanism. Secondly, the kinematic model of the robot is established and solved by analyzing the whole motion and wheel-legged switching motion for the operation requirements under different road conditions. By synthesizing the constraint relationships among the modules and analyzing the robot’s obstacle-crossing abilities, we systematically established the mechanical model of the robot when it encounters obstacles. Thirdly, we studied the stability of the robot based on the stable cone method in the case of slope and unilateral transformation wheel deployment and achieved the tipping condition in the critical state. Finally, we used ADAMS software to simulate and analyze the driving process of the robot in various types of terrain and obstacles in order to verify that it has superior performance through obstacles and motion flexibility. The analysis shows that the robot can passively adapt to various complex and variable obstacle-filled terrains with obstacle heights which are much higher than its center of gravity range. The results of the study can provide a reference for the structural optimization and the obstacle-crossing performance improvement of mobile robots.

Abstract Objective Human intention recognition technology plays a vital role in the application of robotic exoskeletons and powered exoskeletons. However, the precise estimation of the continuous motion of each joint represents a major challenge. In the current study, we present a method for estimating continuous elbow joint movement. Methods We developed a novel approach for estimating the elbow joint angle based on human physiological structure. We used surface electromyography signals to analyze the biomechanical properties of the muscle and combined it with physiological structure to achieve a model for estimating continuous motion. And a genetic algorithm was used to optimize unknown parameters. Results We performed extensive trials to verify the generalizability and effectiveness of this method. The trial types included elbow joint motion with single cycle trials, typical cycle trials, gradually increasing amplitude trials, and random movement trials for handheld loads of 1.25 and 2.5 kg. The results revealed that the average root-mean-square errors ranged from 0.12 to 0.26 rad, reflecting an appropriate level of estimation accuracy. Conclusion Establishing a reasonable physiological model and applying an efficient optimization algorithm enabled more accurate estimation of the joint angle. The proposed method provides a theoretical foundation for robotic exoskeletons and powered exoskeletons to understand the intentions of human continuous motion.

To solve the problems that the theoretical analysis of Halbach array magnetic circuit is insufficient and that calculating the magnetic adsorption force of a permanent magnet by using the magnetic node method is complex, the magnetic flux density of a Halbach array magnetic circuit composed of multiple permanent magnets with perpendicular magnetization directions is calculated. On the basis of the concentrated magnetic phenomenon of the ferromagnetic material and the end effect of the permanent magnet, a method for calculating the magnetic adsorption force of the Halbach array magnetic circuit by using the equivalent magnetic flux density is proposed, and the variation trend of magnetic adsorption force after changing the parameters of the magnetic circuit is obtained. ANSYS software is used to analyze several magnetic circuits that produce large magnetic adsorption force, a magnetic circuit structure that produces the largest magnetic adsorption force is determined, and the permanent magnetic adsorption device of the wall-climbing robot is improved. The magnetic adsorption force of the wall-climbing robot before and after the improvement of the permanent magnetic adsorption device is measured through experiments. The experimental results show that the magnetic adsorption force after the improvement is increased by 24.63% compared to before the improvement.

Aiming at the multi-objective control problem of the tracking effect and vehicle stability in the process of intelligent vehicle trajectory tracking, a coordinated control strategy of the trajectory tracking and stability of intelligent electric vehicles is proposed based on the hierarchical control theory. The vehicle dynamics model and trajectory tracking model are established. In order to tackle the chattering problem in the traditional sliding mode controller, an Adaptive Spiral Sliding Mode controller is designed by taking the derivative of the controller as the upper controller, which is intended to reduce the heading deviation and lateral deviation in the trajectory tracking process whilst ensuring the stability of the vehicle itself. In the lower controller, a four-wheel tire force optimal distribution method is designed. According to the requirements of the upper controller, combined with the yaw stability of the vehicle, the directional control distribution of the four-wheel tire force is realized. A joint simulation model was built based on CarSim and Simulink, and simulation experiments were performed. The results show that the proposed control strategy can effectively control the heading deviation and lateral deviation in the vehicle trajectory tracking while ensuring the lateral stability of the vehicle.

It is challenging to accurately judge the actual end position of the manipulator—regarded as a rigid body—due to the influence of micro-deformation. Its precise and efficient control is a crucial problem. To solve the problem, the Hamilton principle was used to establish the partial differential equation (PDE) dynamic model of the manipulator system based on the infinite dimension of the working environment interference and the manipulator space. Hence, it resolves the common overflow instability problem in the micro-deformable manipulator system modeling. Furthermore, an infinite-dimensional radial basis function neural network compensator suitable for the dynamic model was proposed to compensate for boundary and uncertain external interference. Based on this compensation method, a distributed boundary proportional differential control method was designed to improve control accuracy and speed. The effectiveness of the proposed model and method was verified by theoretical analysis, numerical simulation, and experimental verification. The results show that the proposed method can effectively improve the response speed while ensuring accuracy.

Owing to insufficient illumination of the space station, the image information collected by the intelligent robot will be degraded, and it will not be able to accurately identify the tools required for the robot’s on-orbit maintenance. This situation increases the difficulty of the robot’s maintenance in a low-illumination environment. We proposes a novel enhancement method for images under low-illumination, namely, a deep learning algorithm based on the combination of deep convolutional and Wasserstein generative adversarial networks (DC-WGAN) in CIELAB color space. The original low-illuminance image is converted from the RGB space to the CIELAB color space which is relatively close to human vision, to accurately estimate the illumination image, and effectively reduce the effect of uneven illumination. DC-WGAN is applied to enhance the brightness component by increasing the width of the generation network to obtain more image features. Subsequently, the LAB is converted into RGB space to obtain the final enhanced image. The feasibility of the algorithm is verified by experiments on low-illuminance image under general, special, and actual conditions and comparing the experimental results with four commonly used algorithms. This study lays a technical foundation for robot target recognition and on-orbit maintenance in a space environment.

Recently, numerous wall-climbing robots have been developed for petrochemical tank maintenance. However, most of them are difficult to be widely applied due to common problems such as poor adsorption capacity, low facade adaptability, and low detection accuracy. In order to realize automatic precise detection, an innovative wall-climbing robot system was designed. Based on magnetic circuit optimization, a passive adaptive moving mechanism that can adapt to the walls of different curvatures was proposed. In order to improve detection accuracy and efficiency, a flexible detection mechanism combining with a hooke hinge that can realize passive vertical alignment was designed to meet the detection requirements. Through the analysis of mechanical models under different working conditions, a hierarchical control system was established to complete the wall thickness and film thickness detection. The results showed that the robot could move safely and stably on the facade, as well as complete automatic precise detection.

An integrated method for fault detection of bearing using wavelet packet energy (WPE) and fast kurtogram (FK) is proposed. The method consists of three stages. Firstly, several commonly used wavelet functions were compared to select the appropriate wavelet function for the application of WPE. Then the analyzed signal is decomposed using WPE and the energy of each decomposed signal is calculated and selected for signal reconstruction. Secondly, the reconstructed signal is analyzed by FK to select the best central frequency and bandwidth for the band-pass filter. Finally, the filtered signal is processed using the squared envelope frequency spectrum and compared with the theoretical fault characteristic frequency for fault feature extraction. The procedure and performance of the proposed approach are illustrated and estimated by the simulation analysis, proving that the proposed method can effectively extract the weak transients. Moreover, the analysis results of gearbox bearing and rolling bearing cases show that the proposed method can provide more accurate fault features compared with the individual FK method.

Weld detection is vital to the quality of ship construction and navigation safety, and numerous detection robots have been developed and widely applied. Focusing on the current bottleneck of robot safety, efficiency, and intelligent detection, this paper developed a wall-climbing robot that integrates multiple sensors and uses fluorescent magnetic powder for nondestructive testing. We designed a moving mechanism that can safely move on a curved surface and a serial-parallel hybrid flexible detection mechanism that incorporates a force sensor to solve the robot’s safe adsorption and a flexible detection of the curved surface to complete the flaw detection operation. We optimized the system structure and improved the overall performance of the robot by establishing a unified mechanical model for different operating conditions. Based on the collected sensor information, a multi-degree of freedom component collaborative flexible detection method with a standard detecting process was developed to complete efficient, high-quality detection. Results showed that the developed wall-climbing robot can move safely and steadily on the complex facade and can complete the flaw detection of wall welds.

Increasing requirements for the safety of human-robot interaction and the cost-effectiveness of collision detection rapidly promote the development of collision detection technology without torque sensors. To address nonlinear disturbance factors in collision detection that may cause unstable or even incorrect detection, this paper proposed a research strategy that considered the friction as the disturbance term in manipulator motion for the collision detection. The manipulator joint disturbance model was established based on the LuGre dynamic friction model, and the external torque observer was designed based on the generalized momentum. Then, the friction measurement was realized using the external torque observer, and the model parameters were identified through the genetic algorithm. The collision detection can be reduced errors after the friction model by compensating the disturbance and can be applicable to variable working conditions. Finally, the accuracy of the constructed disturbance model and the performance of the proposed collision detection method were validated by the experimental studies.

A unit module of the mobile modular reconfigurable robot (M 2 rBot) with the 12 connection ways is first introduced. For expressing the spatial connection relations and the traversing paths of the M 2 rBot, a novel method of the automatic generation of the spatial connection matrix is presented based on our proposed double-module space pose transformation. The motion equations are also automatically generated to solve the reconstruction problems of mechanical configuration, kinematics, and dynamics. This method enables to analyze the kinematics and dynamics for both serial and multi-branch modular robots with different configurations. As for the control strategies, a fuzzy robust adaptive controller is also developed to adapt to the different configurations without adjusting any control parameters. The results show that the novel method is effective, and the proposed controller can satisfy tracking performances and quickly adapt to the change of the configurations, the frictions, and the external disturbances. The proposed method and controller are easy to extend to other modular robots.

Hydrodynamic modeling is the basis of the precise control research of underwater manipulators. Viscous hydrodynamics, an important part of the hydrodynamic model, directly affects the accuracy of the dynamic model and the control model of the manipulator. Considering the limited research on viscous hydrodynamics of underwater manipulators and the difficulty in measuring viscous hydrodynamic coefficients, the viscous hydrodynamic model in the form of Taylor expansion is analyzed and established. Through carrying out simulation calculations, curve fitting and regression analysis, positional derivatives, rotational derivatives, and coupling derivatives in the viscous hydrodynamic model, are determined. This model provides a crucial theoretical foundation and reference data for subsequent related research.

As the foundation of model control, robot dynamics is crucial. However, a robot is a complex multi-input–multi-output system. System noise seriously affects parameter identification results, thereby inevitably requiring us to conduct signal processing to extract useful signals from chaotic noise. In this research, the dynamic parameters were identified on the basis of the proposed multi-criteria embedded optimization design method, to obtain the optimal excitation signal and then use maximum likelihood estimation for parameter identification. Considering the movement coupling characteristics of the multi-axis, experiments were based on a two degrees-of-freedom manipulator with joint torque sensors. Simulation and experimental results showed that the proposed method can reasonably resolve the problem of mutual opposition within a single criterion and improve the identification robustness in comparison with other optimization criteria. The mean relative standard deviation was 0.04 and 0.3 lower in the identified parameters than in F1 and F3, respectively, thus signifying that noise is effectively alleviated. In addition, validation experimental curves were close to the estimation model, and the average of root mean square (RMS) is 0.038, thereby confirming the accuracy of the proposed method.