Clear, correct imaging is a prerequisite for underwater operations. In real freshwater environment including rivers and lakes, the water bodies are usually turbid and dynamic, which brings extra troubles to quality of imaging due to color deviation and suspended particulate. Most of the existing underwater imaging methods focus on relatively clear underwater environment, it is uncertain that if those methods can work well in turbid and dynamic underwater environments. In this paper, we propose a turbidity-adaptive underwater image enhancement method. To deal with attenuation and scattering of varying degree, the turbidity is detected by the histogram of images. Based on the detection result, different image enhancement strategies are designed to deal with the problem of color deviation and blurring. The proposed method is verified by an underwater image dataset captured in real underwater environment. The result is evaluated by image metrics including structure similarity index measure, underwater color image quality evaluation metric, and speeded-up robust features. Test results exhibit that the method can correct the color deviation and improve the quality of underwater images.

Existing valveless piezoelectric pumps are mostly based on the flow resistance mechanism to generate unidirectional fluid pumping, resulting in inefficient energy conversion because the majority of mechanical energy is consumed in terms of parasitic loss. In this paper, a novel tube structure composed of a Y-shaped tube and a ȹ-shaped tube was proposed considering theory of jet inertia and vortex dissipation for the first time to improve energy efficiency. After verifying its feasibility through the flow field simulation, the proposed tubes were integrated into a piezo-driven chamber, and a novel valveless piezoelectric pump with the function of rectification (NVPPFR) was reported. Unlike previous pumps, the reported pump directed the reflux fluid to another flow channel different from the pumping fluid, thus improving pumping efficiency. Then, mathematical modeling was established, including the kinetic analysis of vibrator, flow loss analysis of fluid, and pumping efficiency. Eventually, experiments were designed, and results showed that NVPPFR had the function of rectification and net pumping effect. The maximum flow rate reached 6.89 mL/min, and the pumping efficiency was up to 27%. The development of NVPPFR compensated for the inefficiency of traditional valveless piezoelectric pumps, broadening the application prospect in biomedicine and biology fields.

With the proposal of intelligent mines, unmanned mining has become a research hotspot in recent years. In the field of autonomous excavation, environmental perception and excavation trajectory planning are two key issues because they have considerable influences on operation performance. In this study, an unmanned electric shovel (UES) is developed, and key robotization processes consisting of environment modeling and optimal excavation trajectory planning are presented. Initially, the point cloud of the material surface is collected and reconstructed by polynomial response surface (PRS) method. Then, by establishing the dynamical model of the UES, a point to point (PTP) excavation trajectory planning method is developed to improve both the mining efficiency and fill factor and to reduce the energy consumption. Based on optimal trajectory command, the UES performs autonomous excavation. The experimental results show that the proposed surface reconstruction method can accurately represent the material surface. On the basis of reconstructed surface, the PTP trajectory planning method rapidly obtains a reasonable mining trajectory with high fill factor and mining efficiency. Compared with the common excavation trajectory planning approaches, the proposed method tends to be more capable in terms of mining time and energy consumption, ensuring high-performance excavation of the UES in practical mining environment.

The use of artificial intelligence to process sensor data and predict the dimensional accuracy of machined parts is of great interest to the manufacturing community and can facilitate the intelligent production of many key engineering components. In this study, we develop a predictive model of the dimensional accuracy for precision milling of thin-walled structural components. The aim is to classify three typical features of a structural component—squares, slots, and holes—into various categories based on their dimensional errors (i.e., “high precision,” “pass,” and “unqualified”). Two different types of classification schemes have been considered in this study: those that perform feature extraction by using the convolutional neural networks and those based on an explicit feature extraction procedure. The classification accuracy of the popular machine learning methods has been evaluated in comparison with the proposed deep learning model. Based on the experimental data collected during the milling experiments, the proposed model proved to be capable of predicting dimensional accuracy using cutting parameters (i.e., “static features”) and cutting-force data (i.e., “dynamic features”). The average classification accuracy obtained using the proposed deep learning model was 9.55% higher than the best machine learning algorithm considered in this paper. Moreover, the robustness of the hybrid model has been studied by considering the white Gaussian and coherent noises. Hence, the proposed hybrid model provides an efficient way of fusing different sources of process data and can be adopted for prediction of the machining quality in noisy environments.

Physical models carry quantitative and explainable expert knowledge. However, they have not been introduced into gas face seal diagnosis tasks because of the unacceptable computational cost of inferring the input fault parameters for the observed output or solving the inverse problem of the physical model. The presented work develops a surrogate-model-assisted method for solving the nonlinear inverse problem in limited physical model evaluations. The method prepares a small initial database on sites generated with a Latin hypercube design and then performs an iterative routine that benefits from the rapidity of the surrogate models and the reliability of the physical model. The method is validated on simulated and experimental cases. Results demonstrate that the method can effectively identify the parameters that induce the abnormal signal output with limited physical model evaluations. The presented work provides a quantitative, explainable, and feasible approach for identifying the cause of gas face seal contact. It is also applicable to mechanical devices that face similar difficulties.

Product innovation is often a process for improving existing products. Low-end disruptive innovation (LDI) enables a product to meet the most price-sensitive customers in the low-end market. The existing LDI methods are mainly based on unnecessary characteristics of disruptive innovations. Thus, they cannot easily identify and respond to the LDI design needs. This study proposes a hybrid method for the product LDI in two levels of the product design based on the summarized definition and essential characteristics of LDI. Feasible areas of the product LDI are determined using a hybrid relational function model to identify the maturity of dominant technologies. The technologies are identified through the technical search and evaluation of the feasible area for innovation to form an initial LDI scheme. Then, the product function is optimized using the trimming concept of theory of inventive problem solving based on the characteristics of LDI. The final LDI scheme is formed and evaluated based on the essential characteristics of the product LDI. The feasibility of the proposed method is verified in the design of a new dropping pill machine.

This study traces the development of dexterous hand research and proposes a novel antagonistic variable stiffness dexterous finger mechanism to improve the safety of dexterous hand in unpredictable environments, such as unstructured or man-made operational errors through comprehensive consideration of cost, accuracy, manufacturing, and application. Based on the concept of mechanical passive compliance, which is widely implemented in robots for interactions, a finger is dedicated to improving mechanical robustness. The finger mechanism not only achieves passive compliance against physical impacts, but also implements the variable stiffness actuator principle in a compact finger without adding supererogatory actuators. It achieves finger stiffness adjustability according to the biologically inspired stiffness variation principle of discarding some mobilities to adjust stiffness. The mechanical design of the finger and its stiffness adjusting methods are elaborated. The stiffness characteristics of the finger joint and the actuation unit are analyzed. Experimental results of the finger joint stiffness identification and finger impact tests under different finger stiffness presets are provided to verify the validity of the model. Fingers have been experimentally proven to be robust against physical impacts. Moreover, the experimental part verifies that fingers have good power, grasping, and manipulation performance.

Axial piston pumps have wide applications in hydraulic systems for power transmission. Their condition monitoring and fault diagnosis are essential in ensuring the safety and reliability of the entire hydraulic system. Vibration and discharge pressure signals are two common signals used for the fault diagnosis of axial piston pumps because of their sensitivity to pump health conditions. However, most of the previous fault diagnosis methods only used vibration or pressure signal, and literatures related to multi-sensor data fusion for the pump fault diagnosis are limited. This paper presents an end-to-end multi-sensor data fusion method for the fault diagnosis of axial piston pumps. The vibration and pressure signals under different pump health conditions are fused into RGB images and then recognized by a convolutional neural network. Experiments were performed on an axial piston pump to confirm the effectiveness of the proposed method. Results show that the proposed multi-sensor data fusion method greatly improves the fault diagnosis of axial piston pumps in terms of accuracy and robustness and has better diagnostic performance than other existing diagnosis methods.

Cable-driven parallel robot (CDPR) is a type of high-performance robot that integrates cable-driven kinematic chains and parallel mechanism theory. It inherits the high dynamics and heavy load capacities of the parallel mechanism and significantly improves the workspace, cost and energy efficiency simultaneously. As a result, CDPRs have had irreplaceable roles in industrial and technological fields, such as astronomy, aerospace, logistics, simulators, and rehabilitation. CDPRs follow the cutting-edge trend of rigid–flexible fusion, reflect advanced lightweight design concepts, and have become a frontier topic in robotics research. This paper summarizes the kernel theories and developments of CDPRs, covering configuration design, cable-force distribution, workspace and stiffness, performance evaluation, optimization, and motion control. Kinematic modeling, workspace analysis, and cable-force solution are illustrated. Stiffness and dynamic modeling methods are discussed. To further promote the development, researchers should strengthen the investigation in configuration innovation, rapid calculation of workspace, performance evaluation, stiffness control, and rigid–flexible coupling dynamics. In addition, engineering problems such as cable materials, reliability design, and a unified control framework require attention.

The machining unit of hobbing machine tool accounts for a large portion of the energy consumption during the operating phase. The optimization design is a practical means of energy saving and can reduce energy consumption essentially. However, this issue has rarely been discussed in depth in previous research. A comprehensive function of energy consumption of the machining unit is built to address this problem. Surrogate models are established by using effective fitting methods. An integrated optimization model for reducing tool displacement and energy consumption is developed on the basis of the energy consumption function and surrogate models, and the parameters of the motor and structure are considered simultaneously. Results show that the energy consumption and tool displacement of the machining unit are reduced, indicating that energy saving is achieved and the machining accuracy is guaranteed. The influence of optimization variables on the objectives is analyzed to inform the design.

This study presents a family of novel translational parallel mechanisms (TPMs) with single-loop topological structures. The proposed mechanism consists of only revolute and prismatic joints. The novel TPMs are simpler in structure and have fewer joints and components than the well-known Delta Robot. Four types of 2-degree of freedom driving systems are applied to different limb structures to avoid the moving actuator that causes the problem of increased moving mass. Four sample TPMs are constructed using the synthesized limbs, and one of them is investigated in terms of kinematic performance. First, a position analysis is performed and validated through numerical simulation to reveal the characteristics of partially decoupled motion, which improves the controllability of TPM. Second, singular configurations are identified, and the resulting singularity curve is obtained. Lastly, the workspace of TPM is analyzed, and the relationship between the singular configurations and the reachable workspace is explored. The workspace of the 3-CRR (C denotes the cylindrical joint and R denotes the revolute joint) translational mechanism is also presented to prove that the proposed TPM has a fairly large workspace.

The loss of hand functions in upper limb amputees severely restricts their mobility in daily life. Wearing a humanoid prosthetic hand would be an effective way of restoring lost hand functions. In a prosthetic hand design, replicating the natural and dexterous grasping functions with a few actuators remains a big challenge. In this study, a function-oriented optimization design (FOD) method is proposed for the design of a tendon-driven humanoid prosthetic hand. An optimization function of different functional conditions of full-phalanx contact, total contact force, and force isotropy was constructed based on the kinetostatic model of a prosthetic finger for the evaluation of grasping performance. Using a genetic algorithm, the optimal geometric parameters of the prosthetic finger could be determined for specific functional requirements. Optimal results reveal that the structure of the prosthetic finger is significantly different when designed for different functional requirements and grasping target sizes. A prosthetic finger was fabricated and tested with grasping experiments. The mean absolute percentage error between the theoretical value and the experimental result is less than 10%, demonstrating that the kinetostatic model of the prosthetic finger is effective and makes the FOD method possible. This study suggests that the FOD method enables the systematic evaluation of grasping performance for prosthetic hands in the design stage, which could improve the design efficiency and help prosthetic hands meet the design requirements.

Small pipes exist in industrial and biomedical fields, and require microrobots with high operational precision and large load capacity to inspect or perform functional tasks. A piezoelectric inertial pipeline robot using a “stick-slip” mechanism was proposed to address this requirement. In this study, the driving principle of the proposed robot was analyzed, and the strategy of the design scheme was presented. A dynamics model of the stick-slip system was established by combining the dynamics model of the driving foot system and the LuGre friction model, and the simulation analysis of the effect of system parameters on the operating trajectory was performed. An experimental system was established to examine the output characteristics of the proposed robot. Experimental results show that the proposed pipeline robot with inertial stick-slip mechanism has a great load capacity of carrying 4.6 times (70 g) its own mass and high positioning accuracy. The speed of the pipeline robot can reach up to 3.5 mm/s (3 mm/s) in the forward (backward) direction, with a minimum step distance of 4 μm. Its potential application for fine operation in the pipe is exhibited by a demonstration of contactless transport.

Robotic-assisted surgical system has introduced a powerful platform through dexterous instrument and hand−eye coordination intuitive control. The knowledge of laparoscopic vision is a crucial piece of information for robot-assisted minimally invasive surgery focusing on improved surgical outcomes. Obtaining the transformation with respect to the laparoscope and robot slave arm frames using hand−eye calibration is essential, which is a key component for developing intuitive control algorithm. We proposed a novel two-step modified dual quaternion for hand−eye calibration in this study. The dual quaternion was exploited to solve the hand−eye calibration simultaneously and powered by an iteratively separate solution. The obtained hand−eye calibration result was applied to the intuitive control by using the hand−eye coordination criterion. Promising simulations and experimental studies were conducted to evaluate the proposed method on our surgical robot system. We extensively compared the proposed method with state-of-the-art methods. Results demonstrate this method can improve the calibration accuracy. The effectiveness of the intuitive control algorithm was quantitatively evaluated, and an improved hand−eye calibration method was developed. The relationship between laparoscope and robot kinematics can be established for intuitive control.

Many organisms have attachment organs with excellent functions, such as adhesion, clinging, and grasping, as a result of biological evolution to adapt to complex living environments. From nanoscale to macroscale, each type of adhesive organ has its own underlying mechanisms. Many biological adhesive mechanisms have been studied and can be incorporated into robot designs. This paper presents a systematic review of reversible biological adhesive methods and the bioinspired attachment devices that can be used in robotics. The study discussed how biological adhesive methods, such as dry adhesion, wet adhesion, mechanical adhesion, and sub-ambient pressure adhesion, progress in research. The morphology of typical adhesive organs, as well as the corresponding attachment models, is highlighted. The current state of bioinspired attachment device design and fabrication is discussed. Then, the design principles of attachment devices are summarized in this article. The following section provides a systematic overview of climbing robots with bioinspired attachment devices. Finally, the current challenges and opportunities in bioinspired attachment research in robotics are discussed.

The multi-material assembled light alloy wheel presents an effective lightweight solution for new energy vehicles, but its riveting connection remains a problem. To address this problem, this paper proposed the explicit riveting-implicit springback-implicit fatigue/explicit impact sequence coupling simulation analysis method, analyzed the fatigue and impact performance of the punching riveting connected magnesium/aluminum alloy (Mg/Al) assembled wheel, and constructed some major evaluation indicators. The accuracy of the proposed simulation method was verified by conducting physical experiments of single and cross lap joints. The punching riveting process parameters of the assembled wheel joints were defined as design variables, and the fatigue and impact performance of the assembled wheel was defined as the optimization objective. The connection-performance integration multi-objective optimization design of the assembled wheel considering riveting residual stress was designed via Taguchi experiment, grey relational analysis, analytic hierarchy process, principal component analysis, and entropy weighting methods. The optimization results of the three weighting methods were compared, and the optimal combination of design variables was determined. The fatigue and impact performance of the Mg/Al assembled wheel were effectively improved after optimization.