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Abstract Laminectomy is one of the most common posterior spinal operations. Since the lamina is adjacent to important tissues such as nerves, once damaged, it can cause serious complications and even lead to paralysis. In order to prevent the above injuries and complications, ultrasonic bone scalpel and surgical robots have been introduced into spinal laminectomy, and many scholars have studied the recognition method of the bone tissue status. Currently, almost all methods to achieve recognition of bone tissue are based on sensor signals collected by high‐precision sensors installed at the end of surgical robots. However, the previous methods could not accurately identify the state of spinal bone tissue. Innovatively, the identification of bone tissue status was regarded as a time series classification task, and the classification algorithm LSTM‐FCN was used to process fusion signals composed of force and cutting depth signals, thus achieving an accurate classification of the lamina bone tissue status. In addition, it was verified that the accuracy of the proposed method could reach 98.85% in identifying the state of porcine spinal laminectomy. And the maximum penetration distance can be controlled within 0.6 mm, which is safe and can be used in practice.

Abstract Existing mobile robots mostly use graph search algorithms for path planning, which suffer from relatively low planning efficiency owing to high redundancy and large computational complexity. Due to the limitations of the neighborhood search strategy, the robots could hardly obtain the most optimal global path. A global path planning algorithm, denoted as EDG*, is proposed by expanding nodes using a well-designed expanding disconnected graph operator (EDG) in this paper. Firstly, all obstacles are marked and their corners are located through the map pre-processing. Then, the EDG operator is designed to find points in non-obstruction areas to complete the rapid expansion of disconnected nodes. Finally, the EDG* heuristic iterative algorithm is proposed. It selects the candidate node through a specific valuation function and realizes the node expansion while avoiding collision with a minimum offset. Path planning experiments were conducted in a typical indoor environment and on the public dataset CSM. The result shows that the proposed EDG* reduced the planning time by more than 90% and total length of paths reduced by more than 4.6%. Compared to A*, Dijkstra and JPS, EDG* does not show an exponential explosion effect in map size. The EDG* showed better performance in terms of path smoothness, and collision avoidance. This shows that the EDG* algorithm proposed in this paper can improve the efficiency of path planning and enhance path quality.

IntroductionThe growing global demand for foreign language learning contrasts sharply with the shortage of second language teachers. In Chinese as a second language (CSL) education, although the number of pre-service CSL teachers is increasing, few continue in the profession after completing their teacher education courses.MethodsTo investigate the reasons behind this trend, this longitudinal narrative inquiry examined the career motivations of three participants during their teaching practicum. The study focused on identifying key narrative clues based on metaphors emerging from their narratives.ResultsThe research found that the participants’ career motivations were influenced by their teaching practice and experienced various dynamic changes. Key motivational factors included self-efficacy and intrinsic self-fulfillment, with a notable influence from the unique cross-cultural motivation associated with second language teaching.DiscussionThe study underscores the significant role of narratives and metaphors in understanding changes in teacher career motivations. It suggests that enhancing self-efficacy and intrinsic motivation, alongside recognizing cross-cultural motivations, could be crucial in addressing the retention issues among pre-service CSL teachers.

The performance of Aquatic Unmanned Aerial Vehicle (AquaUAV) has always been limited so far and far from practical applications, due to insufficient propulsion, large-resistance structure etc. Aerial-aquatic amphibians in nature may facilitate the development of AquaUAV since their excellent amphibious locomotion capabilities evolved under long-term natural selection. This article will take four typical aerial-aquatic amphibians as representatives, i.e., gannet, cormorant, flying fish and flying squid. We summarized the multi-mode locomotion process of common aerial-aquatic amphibians and the evolutionary trade-offs they have made to adapt to amphibious environments. The four typical propulsion mechanisms were investigated, which may further inspire the propulsion design of the AquaUAV. And their morphological models could guide the layout optimization. Finally, we reviewed the state of art in AquaUAV to validate the potential value of our bioinspiration, and discussed the future prospects.

Cormorants (Phalacrocoraxe), types of aquatic birds, utilize the compliance/flexibility of the flippers and exploit hydrodynamic/biomechanic processes to accomplish diverse operations. Particularly, the flipper‐propelled locomotion exhibits traits such as super‐redundancy and large deformations, necessitating depiction of both movements of the rigid skeletons as well as local deformations of the soft tissues. However, there are few well‐established kinematic/hydrodynamic framework models and constitutive equations for such rigid–flexible intrinsically coupled biosystems. Herein, combined with a skeletal skinning algorithm to handle the deformation of a flexible body attached to a rigid body, a numerical computation framework for an in‐depth fluid–structure interaction is presented, which enables the capture of viscoelastic and anisotropic characteristics of a highly compliant 3D rigid–flexible coupled model in a low‐Reynolds‐number flow. Considering the biorobotic cormorant flipper with a nonuniformly distributed stiffness as a representative, the challenging issue of controlling a biomechanically compliant flipper to synthesize realistic locomotion sequences, including rigid skeleton movements and soft tissue deformations, is addressed. Furthermore, a numerical computational hydrodynamic analysis is performed to demonstrate that the cormorant flipper can generate 5 N fluid force and 0.45 N m fluid moment during the turning operation in 0.8 s, which is consistent with the former experimental results.

The impact of motivation, anxiety and learning strategies on the achievement of foreign language proficiency has been widely acknowledged in the context of traditional offline classroom settings. However, this issue has not been extensively documented in relation to online learning, which has become the predominant form of language learning during the period of the COVID-19 pandemic. The current study was conducted to investigate the relative prediction of motivation, anxiety and learning strategies for second language achievement among 90 Thai adult learners of Chinese as a foreign language (CFL) who took online Chinese courses. The participants completed a questionnaire dealing with motivation, anxiety, learning strategies, and their Chinese proficiency was measured by self-report and a Chinese vocabulary size test. A series of hierarchical regression analyses revealed two major findings. First, anxiety emerged as the most stable factor for the participants' CFL achievement, followed by learning strategies and motivation. Second, motivation, anxiety and learning strategies only significantly predicted the participants' self-rated Chinese language proficiency, but not their performance on the Chinese vocabulary size test. The overall results indicate the relative importance of motivation, anxiety and learning strategies to Chinese language learning in the online environment and suggest different measures of CFL achievement may lead to different research findings. The general findings were of theoretical and pedagogical significance for understanding and addressing individual differences factors in online language learning.

A neural network embedding learning control scheme is proposed in this paper, which addresses the performance optimization problem of a class of nonlinear system with unknown dynamics and disturbance by combining with a novel nonlinear function approximator and an improved disturbance observer (DOB). We investigated a mixture basic function (MBF) to approximate the unknown nonlinear dynamics of the system, which allows an approximation in a global scope, replacing the traditional radial basis function (RBF) neural networks technique that only works locally and could be invalid beyond some scope. The classical disturbance observer is improved, and some constraint conditions thus are no longer needed. A neural network embedding learning control scheme is exploited. An arbitrary type of neural network can be embedded into a base controller, and the new controller is capable of optimizing the control performance by tuning the parameters of neural network and satisfying the Lyapunov stability simultaneously. Simulation results verify the effectiveness and advantage of our proposed methods.

Existing studies on rehabilitation robots are generally devoted to robot-assisted active rehabilitation training, which is conducive to facilitating muscle and nerve regeneration. However, human–robot interaction (HRI) requires imposing a limit on the workspace within which the robot operates, so as to ensure patient safety. A safe admittance boundary algorithm for a rehabilitation robot is proposed based on the space classification model which works by constructing a virtual boundary for the HRI workspace in the control layer. First, point cloud isodensification is performed for the workspaces within which the human body and the robot operate. Next, the nearest neighbor density is determined for the point cloud, and the space classification model is built on this basis. Finally, the space classification model is integrated with admittance control to derive the safe admittance boundary algorithm, which can be used for safety control. This algorithm is then subjected to space verification experiments and out–of–bounds experiments using a dynamic arm simulator (DAS). As indicated by the experimental results, when the side length of the voxel grid for voxel center filtering is set to 0.06 m, the accuracy of space definition is 98.40%. The average maximum response time for out–of–bounds behaviors is 165.62 ms. The safe admittance boundary algorithm can impose reasonable virtual constraints on the robot workspace, thereby improving HRI safety.

Aquatic unmanned aerial vehicles (AquaUAV) have aroused much attention from researchers, though no fully-featured aerial-aquatic UAV exists so far. The assistance of webbed foot hydroplaning can accomplish rapid take-off of a cormorant. A significant impact force and moment can be generated due to the webbed foot propulsion in the water-to-air transition. However, the change law of force and moment experienced by the cormorant during take-off has not been captured. Based on previous achievements in the biological investigation, we developed a biomimetic prototype with curve fitting model and parameter optimization to attain specific movements to imitate cormorant's hydroplaning strategy. The bionic webbed foot considers the elastic mechanics, and the forepart is regarded as flexible material for fluid-structure interaction (FSI). Dynamic process of rapid take-off in the aspects of flow characteristics and mechanical properties can be estimated by computational fluid dynamics (CFD) in our proposed FSI model, which establishes a foundation for further applications in the design of the assisted propulsion system of aerial-aquatic UAV.

The present research envisages a method for the robotic grasping based on the improved Gaussian mixture model. The improved Gaussian mixture model is a method proposed by incorporating Bayesian ideas into the Gaussian model. It will use the Gaussian model to perform grasping training in a certain area which we called trained area. The improved Gaussian models utilized the trained Gaussian models as prior models. The proposed method improved the cumulative updates and the evaluation results of the improved models to make robots more adaptable to grasp in the untrained areas. The self-taught learning ability of the robot about grasping was semi-supervised. Firstly, the observable variables of objects were determined by a camera. Then, we dragged the robot to grasp object. The relationship between the variables and robot's joint angles were mapped. We obtained new samples in the close untrained area to improve the Gaussian model. With these new observable variables, the robot grasped it successfully. Finally, the effectiveness of the method was verified by experiments and comparative tests on grasping of real objects and grasping simulation of the improved Gaussian models through the virtual robot experimentation platform.

Vascular image registration is an essential approach to combine the advantages of preoperative 3D computed tomography angiography images and intraoperative 2D X-ray/digital subtraction angiography (DSA) images together. Cardiovascular deformation caused by heartbeat and respiration is one of the most vital factors that affect vascular image registration accuracy. Traditional optimized-based registration methods suffer severely from high computational complexity, which hinders the clinical applications of these methods seriously. To overcome these challenges, we developed a novel multi-channel convolutional neural network (MCNN) that combines a CNN with a periodic vascular diameter variation model. Our method is capable of registering simulated DSA images or real DSA images with their corresponding 3D models in a matter of milliseconds. Our presented MCNN model achieves excellent registration results of three different kinds of cardiovascular patients. The mean absolute error of all six transformation parameters of the MCNN model presented in this paper is less than 1 mm or 1°. The improvement to our MCNN model in registration accuracy is larger than 75% over a single-channel CNN model. Our MCNN method performs more effectively and stable than the state-of-the-art intensity-based methods, especially when vascular deformations occur.

Brain extraction is a process of removing non-brain tissue in the brain magnetic resonance (MR) images and serves as a first step towards more delicate brain segmentation. Although many brain extraction methods have been proposed in the literature, most of them are either laborious or time consuming, and lack of instant visualization. This leads to a time lag between image acquisition and comprehensive visualization. Especially for intraoperative image based neurosurgery navigation, the time lag from image acquisition to brain visualization should be reduced as much as possible. In this paper, we propose an end-to-end fast brain extraction and visualization framework. The input is a T1-weighted MR volume and the output is comprehensive brain visualization. An improved brain extraction tool (BET) algorithm is proposed to evolve a 3D active mesh model to fit the brain surface in the 3D image. Then the brain mask is generated per slice using a polygon fill algorithm. At last, a ray-casting volume rendering algorithm is used to visualize the brain surface with the help of the generated mask. All the operations are performed using the modern OpenGL pipelines running on a graphics processing unit (GPU). Experiments were performed on two publicly available datasets and one clinical dataset to compare our method with five state-of-the-art methods including the original BET in terms of segmentation accuracy and time cost. Our method achieved mean Dice coefficients of 96.8%, 97.1%, 98.5% and mean time cost of 361 ms, 341 ms, 502 ms on the three datasets, outperforming all the other methods.

An optical see-through (OST) head-mounted display (HMD) is an appropriate option for an image-guided surgical navigation system (IGS) based on augmented reality (AR). However, the calibration of OST-HMDs has been challenging, since the augmented results can only be observed by the users wearing it. In this paper, we proposed an online calibration method based on the single-point active alignment method (SPAAM) for a HoloLens using a virtual tracking system. The transformation matrices between the world coordinates and the virtual holographic coordinates are calculated using a linear model and decomposed by the singular value decomposition (SVD) algorithm. The surgical workspace is analyzed, and there are nine sampling points for the offline calibration: eight at the vertices and one at the center of a cube of the workspace. 20 groups of data at each sampling point are collected. Five alignments should be implanted in the online calibration. The transformation matrices are compensated for the offset of every use of a HoloLens. To assess the accuracy of the calibration method, in accordance with the RANSAC algorithm, eight groups of additional data at random non-sampling locations are collected. Three different users that are familiar with the calibration procedures performed the calibration. The average distance error of our calibration was below 6 mm, and the rotation error was up to 5°. It is a user-friendly, simple method for the online calibration of OST-HMDs.

Tracking and grasping a moving target is currently a challenging topic in the field of robotics. The current visual servo grasping method is still inadequate, as the real-time performance and robustness of target tracking both need to be improved. A target tracking method is proposed based on improved geometric particle filtering (IGPF). Following the geometric particle filtering (GPF) tracking framework, affine groups are proposed as state particles. Resampling is improved by incorporating an improved conventional Gaussian resampling algorithm. It addresses the problem of particle diversity loss and improves tracking performance. Additionally, the OTB2015 dataset and typical evaluation indicators in target tracking are adopted. Comparative experiments are performed using PF, GPF and the proposed IGPF algorithm. A dynamic target tracking and grasping method for the robot is proposed. It combines an improved Gaussian resampling particle filter algorithm based on affine groups and the positional visual servo control of the robot. Finally, the robot conducts simulation and experiments on capturing dynamic targets in the simulation environment and actual environment. It verifies the effectiveness of the method proposed in this paper.

Ultrasound-based navigation, as a non-invasive and non-radiation image guiding system, is becoming a research focus in minimally invasive navigation surgery, and calibration between an ultrasonic probe and a 3D vision device is one of the key technologies for ultrasonic registration-based navigation. In this paper, a phantom model was designed as a benchmark for calibration. Both iterative closet point (ICP) and coherent point drift (CPD) algorithms are chosen as point cloud registration methods to implement calibration between ultrasonic scanned points and original phantom points to set up the relationship between the ultrasonic probe and the 3D vision device. Because of large topological difference between the ultrasound scanned points and the points from model, ICP algorithm cannot complete the registration, but the CPD algorithm could implement the registration automatically. The average errors of the center point position for each cavity were 1.50, 1.31, and 1.19 mm, respectively, and the average errors of the axis for each cavity were 0.85°, 0.61°, and 0.99°, respectively. Experiment results showed that the average error of calibration by this method satisfies acquirements of most orthopedic surgeries, and the fully automatic implementation of ultrasonic image processing and subsequent calculation is suitable for on-line calibration and verification in surgery.

Robot-assisted fracture reduction surgery should be highly safe and accurate. It is necessary to accurately determine and control the reduction force of the reduction robot. In this article, a fracture reduction robot is designed with the configuration of the 6-universal-prismatic-universal (6-UPU). The vector method is used to analyze the kinematics of the reduction robot. The Jacobian matrix of the reduction robot and the second-order influence coefficient matrix of the acceleration are obtained. The Lagrangian method is adopted to analyze the dynamic of the reduction robot. The muscle contraction force of the femoral shaft fracture is analyzed based on the Hill model to determine the fracture reduction force. According to changing of the reduction force during the fracture reduction operation, a kind of external force estimation method based on force residual analysis is proposed. When there is no external force sensor at the end of the reduction robot, this method can be used to detect the reduction force in real time. According to the displacement, velocity, and output torque of each branch chain, the reduction force during the reduction operation of the reduction robot can be estimated. A simulation system is conducted and the simulation results show that the fracture reduction force can be estimated and accurately tracked in real time, which is of great significance for the safe operation of the fracture reduction robot.

To improve the positioning accuracy of tunnels for anterior cruciate ligament (ACL) reconstruction, we proposed an ACL reconstruction robotic positioning system based on anatomical characteristics. The system includes a preoperative path planning system, an intraoperative path planning system, and a navigation and positioning system. Brahmet line, anterior, and posterior cortical lines are used for registration of preoperative computed tomography (CT) images and intraoperative X-ray images. A new calibrator of C-arm is applied to establish the mapping between medical images and surgical space. Tunnels for ACL reconstruction can be built anatomically by the robot. The accuracy of the path planning system is 1.73 mm in the four dry bones experiments and 2.17 mm in the two cadaver experiments. The accuracy meets the accuracy requirement of ACL construction surgery.

Collecting seafood animals (such as sea cucumbers, sea echini, scallops, etc.) cultivated in shallow water (water depth: ~30 m) is a profitable and an emerging field that requires robotics for replacing human divers. Soft robotics have several promising features (e.g., safe contact with the objects, lightweight, etc.) for performing such a task. In this paper, we implement a soft manipulator with an opposite-bending-and-extension structure. A simple and rapid inverse kinematics method is proposed to control the spatial location and trajectory of the underwater soft manipulator's end effector. We introduce the actuation hardware of the prototype, and then characterize the trajectory and workspace. We find that the prototype can well track fundamental trajectories such as a line and an arc. Finally, we construct a small underwater robot and demonstrate that the underwater soft manipulator successfully collects multiple irregular shaped seafood animals of different sizes and stiffness at the bottom of the natural oceanic environment (water depth: ~10 m).

In order to detect X-corner (or X-point) features more accurately and apace, this article presents a novel and fast detection method based on block-by-block search strategy. Unlike general pixel-by-pixel searching method, the sampling window is first moved along the image block-by-block to find the X-corner candidates rapidly keeping in view the four-step and min-step-distance constraints. During the motion, some overlap is kept between the adjacent sampling windows in order to ensure that all X-corners could have a chance to reside inside, avoiding the possibility of that some X-corners may locate on the edge. Moreover, labeling technology is adopted to prevent duplicate candidates. After the collection of X-corner candidates, the neighborhood variance and centrosymmetry constraints are used to exclude outliers, and the intersection lines is calculated as the sub-pixel position of true X-corner. The experimental results using synthetic and real images show that the presented method approximately takes just about 13 ms to detect 52 X-corners in an image size of 1024 × 768 on a computer having Intel Core i3 CPU at 3.6 GHz and 4GB RAM. The proposed method has faster detection speed compared with the latest methods such as ChESS, SC, and Micron Tracker system while possessing the same or higher detection precision.

A global path planning method is proposed based on improved ant colony optimization according to the slow convergence speed in mobile service robot path planning. The distribution of initial pheromone is determined by the critical obstacle influence factor. The influence factor is introduced into the heuristic information to improve the convergence speed of the algorithm at an early stage. A new pheromone update rule is presented using fuzzy control to change the value of pheromone heuristic factor and expectation heuristic factor, adjusting the evaporation rate in stages. The method achieves fast convergence and guarantees global search capability. Finally, the simulation results show that the improved algorithm not only shortens the running time of global path planning, but also has a higher probability of obtaining a global optimal solution. The convergence speed of the algorithm is better than the traditional ant colony algorithm.

To enhance the absolute position accuracy and solve complex modeling and computational complexity problems in traditional compensation methods for aviation drilling robots, a compensation method based on the extreme learning machine model was proposed in this article. The proposed method, in which the influence of geometric factors and the non-geometric factors of the robot is considered, builds a positional error prediction model based on extreme learning machine. As the input and output training data, the theoretical position and positional errors measured by a high-precision laser tracker were used to train and construct the extreme learning machine model. After the extreme learning machine model was constructed, the positional errors of prediction points could be predicted using the trained extreme learning machine. Then, the drilling robot controller could be directed to compensate for the predicted positional errors. To verify the correctness and effectiveness of the method, a series of experiments were performed with an aviation drilling robot. The experimental results showed that choosing an appropriate number of training points and hidden neurons for extreme learning machine could increase the computational efficiency without decreasing the high absolute position accuracy. The results also show that the average and maximum absolute position accuracy of robot tool center point were improved by 75.89% and 80.93%, respectively.

Skid-steering mobile robots are widely used because of their simple mechanism and robustness. However, due to the complex wheel-ground interactions and the kinematic constraints, it is a challenge to understand the kinematics and dynamics of such a robotic platform. In this paper, we develop an analysis and experimental kinematic scheme for a skid-steering wheeled vehicle based-on a laser scanner sensor. The kinematics model is established based on the boundedness of the instantaneous centers of rotation (ICR) of treads on the 2D motion plane. The kinematic parameters (the ICR coefficient , the path curvature variable and robot speed ), including the effect of vehicle dynamics, are introduced to describe the kinematics model. Then, an exact but costly dynamic model is used and the simulation of this model’s stationary response for the vehicle shows a qualitative relationship for the specified parameters and . Moreover, the parameters of the kinematic model are determined based-on a laser scanner localization experimental analysis method with a skid-steering robotic platform, Pioneer P3-AT. The relationship between the ICR coefficient and two physical factors is studied, i.e., the radius of the path curvature and the robot speed . An empirical function-based relationship between the ICR coefficient of the robot and the path parameters is derived. To validate the obtained results, it is empirically demonstrated that the proposed kinematics model significantly improves the dead-reckoning performance of this skid–steering robot.

This article presented the optimization parameter of a bidirectional soft actuator and evaluated the properties of the actuator. The systematic simulation was conducted to investigate the effect of the top wedged angle (the angle for the wedged shape of the actuator structure) of the chamber on the bending extent of the actuator when it is deflated. We also investigated the width of the actuator and the material combinations of the two layers with the relation to the deformation performance. A mathematical model was also built to reveal the deformation of the actuator as a function of the geometrical parameters of the inner chambers and the material properties. We quantitatively measured the bending radius and the actuation time of the actuator both in air and under water. Digital particle image velocimetry experiments were conducted under water to observe the flow patterns around the actuator. We found that the top wedged angle has a significant effect on the outward bending of the actuator when it is deflated, and 15° was found to be optimal for bending into a larger gripping space. The result shows that the actuator can deform much easier with a bigger width. Utilizing a soft gripper that was built by mounting four actuators to a three-dimensional-printed rigid support, we found that the prototype can grip objects of different sizes, shapes, and material stiffness in amphibious environments.

A re-entry path planning method in omitting areas for service robots is suggested based on dynamic Inver-Over evolutionary algorithms after the robot automatically avoids obstacles. The complete coverage path planning is researched for cleaning service robots. Combined with features of dynamic travelling salesmen problem (DTSP), a local operator is employed for the path planning to enhance real-time dynamic properties of the Inver-Over algorithm. The method addresses the path planning problem that a number of cells undergo dynamic changes over time under work environment of cleaning robots. With simulations and experiments performed, it is discovered that the average relative error is 2.2% between the re-entry path planning and the best path, which validates the effectiveness and feasibility of the method.

To improve the accuracy of calibration of C-arm, and overcome the space limitation in surgery, we proposed a new calibrator for online calibration of C-arm. After the image rectification by a polynomial fitting-based global correction method, the C-arm was assumed as an ideal pinhole model. The relationships between two kinds of spatial calibration errors and the distribution of fiducial points were studied: the performance of FRE (Fiducial Registration Error) and TRE (Target Registration Error) were not consistent, but both were best at the 12 marked points; the TRE decreased with the increase of the uniformity of calibration points distribution, and with the decrease of the distance between the target point and the center of calibration points. A calibrator with 12 fiducial points conical helically distributed, which could be placed on the knee, was an attractive option. A total of 10 experiments on C-arm calibration accuracy were conducted and the mean value of mapping error was 0.41 mm. We designed an ACL reconstruction navigation system and carried out specimen experiments on 4 pairs of dry femur and tibia. The mean accuracy of navigation system was 0.85 mm, which is important to the tunnel positioning for ACL reconstruction.

A guidance law has been designed to guide the small unmanned aerial vehicle towards the predefined horizontal smooth path. The guidance law only needs the mathematical expression for the predefined path, the positions, and the velocities of the vehicle in the horizontal inertial frame. The stability of the guidance law has been demonstrated by the Lyapunov stability arguments. In order to improve the path following performance, one of the parameters of the guidance law is tuned by using the fuzzy logic which will still keep its stability. The simulation experiments in the Matlab/Simulink environment to realize the square-, circular-, and the athletics track-style paths following are given to verify the effectiveness of the proposed method. The simulation results show that the path following performance will be improved with smaller overshoot and oscillation amplitude and shorter arrival time with the parameter tuned.

Two-wheeled self-balancing vehicle system is a kind of naturally unstable underactuated system with high-rank unstable multivariable strongly coupling complicated dynamic nonlinear property. Nonlinear dynamics modeling and simulation, as a basis of two-wheeled self-balancing vehicle dynamics research, has the guiding effect for system design of the project demonstration and design phase. Dynamics model of the two-wheeled self-balancing vehicle is established by importing a TSi ProPac package to the Mathematica software (version 8.0), which analyzes the stability and calculates the Lyapunov exponents of the system. The relationship between external force and stability of the system is analyzed by the phase trajectory. Proportional–integral–derivative control is added to the system in order to improve the stability of the two-wheeled self-balancing vehicle. From the research, Lyapunov exponent can be used to research the stability of hyperchaos system. The stability of the two-wheeled self-balancing vehicle is better by inputting the proportional–integral–derivative control. The Lyapunov exponent and phase trajectory can help us analyze the stability of a system better and lay the foundation for the analysis and control of the two-wheeled self-balancing vehicle system.

Due to its advantages of realizing repeatable experiments, collecting data and isolating key factors, the bio-robotic model is becoming increasingly important in the study of biomechanics. The caudal fin of fish has long been understood to be central to propulsion performance, yet its contribution to manoeuverability, especially for homocercal caudal fin, has not been studied in depth. In the research outlined in this paper, we designed and fabricated a robotic caudal fin to mimic the morphology and the three-dimensional (3D) locomotion of the tail of the Bluegill Sunfish ( Lepomis macrochirus ). We applied heave and pitch motions to the robot to model the movement of the caudal peduncle of its biological counterpart. Force measurements and 2D and 3D digital particle image velocimetry were then conducted under different movement patterns and flow speeds. From the force data, we found the addition of the 3D caudal fin locomotion significantly enhanced the lift force magnitude. The phase difference between the caudal fin ray and peduncle motion was a key factor in simultaneously controlling the thrust and lift. The increased flow speed had a negative impact on the generation of lift force. From the average 2D velocity field, we observed that the vortex wake directed water both axially and vertically, and formed a jet-like structure with notable wake velocity. The 3D instantaneous velocity field at 0.6 T indicated the 3D motion of the caudal fin may result in asymmetry wake flow patterns relative to the mid-sagittal plane and change the heading direction of the shedding vortexes. Based on these results, we hypothesized that live fish may actively tune the movement between the caudal fin rays and the peduncle to change the wake structure behind the tail and hence obtain different thrust and lift forces, which contributes to its high manoeuvrability.

To enhance the perpendicularity accuracy in the robotic drilling system, a normal sensor calibration method is proposed to identify the errors of the zero point and laser beam direction of laser displacement sensors simultaneously. The procedure of normal adjustment of the robotic drilling system is introduced firstly. Next the measurement model of the zero point and laser beam direction on a datum plane is constructed based on the principle of the distance measurement for laser displacement sensors. An extended Kalman filter algorithm is used to identify the sensor errors. Then the surface normal measurement and attitude adjustments are presented to ensure that the axis of the drill bit coincides with the normal at drilling point. Finally, simulations are conducted to study the performance of the proposed calibration method and experiments are carried out on a robotic drilling system. The simulation and experimental results show that the perpendicularity of the hole is within 0.2°. They also demonstrate that the proposed calibration method has high accuracy of parameter identification and lays a basis for high-precision perpendicularity accuracy of drilling in the robotic drilling system.

Here, we propose an autonomous path planning solution using backpropagation algorithm. The mechanism of movement used by humans in controlling their arms is analyzed and then applied to control a robot manipulator. Autonomous path planning solution is a numerical method. The model of industrial robot manipulator used in this article is a KUKA KR 210 R2700 EXTRA robot. In order to show the performance of the autonomous path planning solution, an experiment validation of path tracking is provided. Experiment validation consists of implementation of the autonomous path planning solution and the control of physical robot. The process of converging to target solution is provided. The mean absolute error of position for tool center point is also analyzed. Comparison between autonomous path planning solution and the numerical methods based on Newton–Raphson algorithm is provided to demonstrate the efficiency and accuracy of the autonomous path planning solution.

We have developed a robot system for closed diaphyseal fracture reduction. Because accuracy is essential for the treatment effects of the robot system and for the safety of both the patients and surgeons, we analysed accuracy in a systematic way. Both the structure of the robot and the operation procedure are described. Using the transfer model of errors in series and the error differential solving method for parallel mechanisms, an error model was established, and the main influencing factors of errors were considered. The Monte Carlo method was used to perform the simulation based on the error model. Experiments of image registration, of the mechanism and of the whole robot system were tested in different aspects to verify that the results of the simulation are correct. The system accuracy was compared with clinical standards to show that the robot system fulfilled the requirements for closed diaphyseal fracture reduction. The accuracy analysis method also provides an efficient path for other medical robots.

In this study, analytical techniques and fuzzy logic methods are applied to the dynamic modelling and efficient swimming control of a biomimetic robotic fish, which is actuated by an ionic polymer-metal composite (IPMC). A physical-based model for the biomimetic robotic fish is proposed. The model incorporates both the hydrodynamics of the IPMC tail and the actuation dynamics of the IPMC. The comparison of the results of the simulations and experiments shows the feasibility of the dynamic model. By using this model, we found that the harmonic control of the actuation frequency and voltage amplitude of the IPMC is a principal mechanism through which the robotic fish can obtain high thrust efficiency while swimming. The fuzzy control method, which is based on the knowledge of the IPMC fish's dynamic behaviour, successfully utilized this principal mechanism. By comparing the thrust performance of the robotic fish with other control methods via simulation, we established that the fuzzy controller was able to achieve faster acceleration compared with what could be achieved with a conventional PID controller. The thrust efficiency during a steady state was superior to that with conventional control methods. We also found that when using the fuzzy control method the robotic fish can always swim near a higher actuation frequency, which could obtain both the desired speed and high thrust efficiency.

This paper presents a vision-aided inertial navigation system for small unmanned aerial vehicles (UAVs) in GPS-denied environments. During visual estimation, image features in consecutive frames are detected and matched to estimate the motion of the vehicle with a homography-based approach. Afterwards, the visual measurement is fused with the output of an inertial measurement unit (IMU) by an indirect extended Kalman filter (EKF). A delay-based approach for the measurement update is developed to introduce the visual measurement into the fusion without state augmentation. This method supposes that the estimated error state is stable and invariant during the second half of one visual calculation period. Simulation results indicate that delay-based navigation can reduce the computational complexity by about 20% compared with general augmented Vision/INS (inertial navigation system) navigation, with almost the same estimate accuracy. Real experiments were also carried out to test the performance of the proposed navigation system by comparison with the augmented filter method and a referential GPS/INS navigation.

The independent designs guided by domain technologies of mechatronic system lead to the problem of hard compatibility and the difficulty to reuse resources between the systems. This paper presents an approach based on the reuse for modular mechatronic system, and a general framework for modular mechatronic system is designed. After the analysis of related characteristics of the mechatronic system, the modular principle of mechatronic system is proposed. Secondly, a general framework (F-Model) based on reusability of software and hardware in mechatronic system is developed. Following is to elaborate in details on the parts of software, hardware, and infrastructure of mechatronic system. Finally, 6-DOF industrial robot arm and medical accompany robot are taken as our experiment platform to design system based on F-Model. Results in practical application confirm the value of F-Model for reusing modules in designing mechatronic systems.

Modular robot are said to construct a diversity of morphogenesis with self-assembling strategies. They bring about an adaptive entity to deal with complex tasks. By analyzing integration design in module, perception, and control in detail, a swarm modular robot is presented with self-assembly scenario. Then, these active docking robots use the distance measured by infrared sensors between itself and edge of assembled structural entity as input. We design the fuzzy sets and if-then rules according to the human reasoning in following process. Based on Mamdani-type inference, the fuzzy controller can yield two outputs. The outputs are, respectively, used as steering angle speed and linear speed. Due to the diversity of self-assembled structure by the swarm modular robots, we conduct a series of simulated experiments. The results demonstrate the effectiveness and efficiency of the proposed controller in swarm robots' edge-following process.