Robotic hand exoskeletons hold immense potential for enhancing human hand functionality, addressing the hand's strength limitations and fatigue during physically-demanding tasks. However, most existing hand exoskeletons are motorized, being weak in generating high supporting force for gripping augmentation. We present a non-motorized hand exoskeleton based on magnetorheological (MR) actuators to provide high gripping support and elevate grip endurance. Meanwhile, it ingeniously harnesses human energy for actuation and energy storage, enhancing grip strength without external power. The MR actuator demonstrates a peak holding force of 1046 N with merely 5 W power input, boasting a force-to-power ratio one-order-of-magnitude higher than conventional approaches, and 97.7% energy reduction for same holding force compared to other approaches. Participants wearing the hand exoskeletons experience a 41.8% enhancement in grip strength without external power and reduced hand muscle fatigue during prolonged physical labor. In rescuing scenarios such as post-earthquake rescue, debris clearance, and casualty evacuation, our exoskeleton effectively supports gripping and improves working efficiency.

The stability analysis is studied for$H_{\infty } $controlled networked active quarter-vehicle suspension systems with a resilient event-triggered scheme (RETS) under periodic denial-of-service (DoS) jamming attacks in this article. For the networked suspension system, the system-state signals are measured by sensors and transmitted to the cloud controller through a wireless network and then the control signal is transferred to the actuator to control it. An event-triggered scheme (ETS) is designed to reduce the workload of data transmission, which is effective to select some most useful information to transmit and discard some redundant data. DoS attacks can block the data transmission when it is active, so a resilient event-triggered$H_{\infty } $control method is built based on the Lyapunov stability theory. The exponential stability of the controlled suspension system, as well as the$H_{\infty } $performance, is analyzed in this article. Some simulation results show that the proposed control method is effective to improve driving comfort and driving safety and reduce the workload of data transmission under periodic DoS attacks.

Boresight angle misalignment is a major error source in a mobile LiDAR system (MLS), which directly affects the overall accuracy and quality of MLS scanned point clouds data. However, the current calibration of the boresight angle misalignment mainly relies on artificial target features or a manual adjustment, and the intensive labors dramatically limit the calibration flexibility. To solve these problems, this paper develops a novel on-site calibration method for boresight angle misalignment based on multiple natural features constraints, which can automatically incorporate multiple natural features extracted from surrounding environments to generate more accurate calibration results for MLS boresight angle without used any artificial targets or specific facilities. First of all, an improved 4-points congruent sets (I-4PCS) algorithm is proposed for registering the MLS point clouds in forward and backward scanned overlapping areas and realizing smooth global registration for point clouds data. Secondly, a weight principal component analysis (WPCA) approach is presented to automatically extract the appropriate multiple natural features from the well registered point clouds and establish the appropriate features representation. Thirdly, according to the extracted multiple features, the certain geometric constrains equations for spherical, linear/cylindrical, planar features are established based on a model adjustment strategy. Lastly, the boresight angle misalignment calibration can be achieved through fitting the corresponding geometric constrains equations and minimizing the weighted through a least-squares adjustment process. The experimental results demonstrate that the proposed method can effectively on-site calibrate the boresight angle misalignment error, and the overall performance of MLS is significantly improved after the calibration based on multiple natural features constraints.

The platoon of connected autonomous vehicles plays an essential role in future intelligent transportation. It can improve traffic efficiency and release traffic congestion. However, there are lots of existing challenging problems of the control of connected autonomous vehicles, such as the negative impact caused by wireless communication and disturbance. To solve these challenges, a multi-objective asymmetric sliding mode control strategy is proposed in this paper. Firstly, the asymmetric degree is introduced in the topological matrix. Then, a sliding mode controller is designed targeting platoon’s tracking performance. Moreover, Lyapunov analysis are used via Riccati inequality to find the controller’s gains and guarantee internal stability and Input-to-output string stability. Finally, a non-dominated sorting genetic algorithm is utilized to find the Pareto optimal asymmetric degree regarding the overall performance of the platoon, including tracking index, fuel consumption, and acceleration standard deviation. Four different information flow topologies, including a random topology are studied. The results indicate that the proposed asymmetric sliding mode controller can ensure platoon’s stability while improving its performance. The tracking ability is improved by 54.61% and 75.17%, fuel economy is improved by 0.78% and 6.34% under the Urban Road and Highway Case Study, respectively.

Soft sensors are widely used to predict quality variables which are usually hard to measure. It is necessary to construct an adaptive model to cope with process non-stationaries. In this study, a novel quality-related locally weighted soft sensing method is designed for non-stationary processes based on a Bayesian network with latent variables. Specifically, a supervised Bayesian network is proposed where quality-oriented latent variables are extracted and further applied to a double-layer similarity measurement algorithm. The proposed soft sensing method tries to find a general approach for non-stationary processes via quality-related information where the concepts of local similarities and window confidence are explained in detail. The performance of the developed method is demonstrated by application to a numerical example and a debutanizer column. It is shown that the proposed method outperforms competitive methods in terms of the accuracy of predicting key quality variables.

Accurate positioning is an essential requirement of autonomous vehicular navigation system (AVNS) for safe driving. Although the vehicle position can be obtained in global position system friendly environments, in GPS denied environments (such as suburb, tunnel, forest, or underground scenarios) the positioning accuracy of AVNS is easily reduced by the trajectory error of the vehicle. In order to solve this problem, the plane, sphere, cylinder and cone are often selected as the ground control targets to eliminate the trajectory error for AVNS. However, these targets usually suffer from the limitations of incidence angle, measuring range, scanning resolution, and point cloud density, etc. To bridge this research gap, an adaptive continuum shape constraint analysis (ACSCA) method is presented in this article to design a new target with optimized identifiable specific shape to eliminate the trajectory error for AVNS. First of all, according to the proposed ACSCA method, we conduct extensive numerical simulations to explore the optimal ranges of the vertexes and the faces for target shape design, and based on these trials, the optimal target shape is found as icosahedron, which composes of ten vertexes, 20 faces and combines the properties of plane and volume target. Moreover, the algorithm of automatic detection and coordinate calculation is developed to recognize the icosahedron target and calculate its coordinates information for AVNS. Finally, a series of experimental investigation were performed to evaluate the effectiveness of the designed icosahedron target in GPS denied environments. The experimental results demonstrate that compared with the plane, sphere, cylinder and cone targets, the developed icosahedron target can produce better performances than the above targets in terms of the clustered minimum registration error, ambiguity and range of field-of-view; also can significantly improve the positioning accuracy of AVNS in GPS denied environments.

The accuracy of geometric parameters (mainly referred to the incidence angle and measuring distance) in a terrestrial laser scanner (TLS) is not only influenced by the TLS intrinsic systematic instrumental error but also the extrinsic received intensity data. However, the current error compensation methods for geometric parameters mainly focus on the calibration of TLS intrinsic systematic instrumental error and rarely consider the extrinsic intensity data correction. For this reason, this article presents a new method integrating the TLS intrinsic systematic instrumental error calibration and extrinsic intensity data correction to compensate the TLS geometric parameter error. The error compensation procedure is implemented as follows. First, the error compensation mathematical model integrated with TLS intrinsic systematic instrumental error calibration parameters and extrinsic intensity data correction coefficient is established. Second, the hybrid harmonic analysis (HA) and the adaptive wavelet neural network (AWNN) algorithm are proposed to calculate the TLS incidence angle error compensation values. Subsequently, the cubic spline interpolation (CSI) is applied to compute the measuring distance error compensate values. Finally, the TLS (model FARO Focus S150) and the hemispherical angle calibration instrument were used to evaluate the proposed compensation method. The experimental results demonstrate that the geometric parameters are significantly influenced by the intensity data received from TLS, and the proposed method can effectively improve the overall accuracy of the TLS incidence angle and measuring distance.

Robotic wire arc additive manufacturing (WAAM) for the fabrication of metallic parts is garnering interest due to its advantages of low capital investment, high deposition rates, and good material properties. Although many achievements have been made, the build direction of WAAM remains confined to the vertical-up direction, requiring additional supporting structure to be deposited while fabricating metallic parts with overhanging features. In this article, a novel multidirectional WAAM process, using robotic gas metal arc welding, to additively manufacture metal components in multiple directions is presented. Several novel modules, including positional bead modeling, multidirection slicing, and deposition process optimization of the deposition process, are highlights of this article. In addition, the performance of the proposed multidirectional WAAM strategy is evaluated by the successful deposition of a sample workpiece with complex geometrical features. The proposed multidirectional WAAM process would significantly reduce the manufacturing time and cost.

In this paper, a four-wheel electric braking system configuration is proposed for electric vehicles and its braking performance is compared with other conventional braking system configurations at different initial vehicle speeds and different road conditions in the case of emergency braking. In order to make the vehicle wheel slip ratio track the optimal slip ratio, a control method that combines sliding mode control and extended state observer is designed. Neural-network sliding mode control is designed for the driver's braking command tracking in the normal braking condition. In order to improve braking energy recovery, a new braking torque distribution strategy is developed for the proposed four-wheel electric braking system based on the motor characteristics and vehicle dynamics. The designed braking torque distribution strategy is able to improve the energy recovery by adjusting the braking torque distribution ratio between the front and rear wheel braking torque while tracking the driver's braking command. Numerical simulations have been conducted and the simulation results show that although the braking performance of the four-wheel electric braking system is worse than the conventional braking system at high initial braking speed, it still is able to meet the vehicle braking international standards and simplifies the braking system structure and saves cost. The proposed braking torque distribution strategy can improve energy recovery efficiency compared with the average allocation strategy and deceleration based allocation strategy. The simulation results show that the four-wheel electric braking system configuration with the proposed braking torque distribution strategy is suitable for low to medium speed light electric vehicles.

Liquid metal is an interesting metallic material with many unique properties that can be applied in many applications. Liquid metal droplets can be activated by an external electrical field in aqueous environments, which has led to the development of novel actuators. However, a study on the development and control of liquid metal actuating robots is still absent, which hinders their further applications. In this paper, we report the development of a novel controllable untethered vehicle driven by electrically actuated liquid metal droplets in a sodium hydroxide solution. The simplified dynamic model of the vehicle in sodium hydroxide solution was developed. The vehicle's performance, including translational and rotating locomotion with various speeds, was experimentally evaluated. The vehicle driven by liquid metal droplets possesses many advantages such as working silently, almost wear-free motion, and low power consumed, which has great potential to be applied in liquid metal enabled robotics and automation process such as laboratory automation.

As the concept of variable stiffness (VS) and variable damping (VD) has increasingly drawn attention because of its superiority on reducing unwanted vibrations, dampers with property of varying stiffness and damping have been an attractive method to further improve vehicle performance and driver comfort. This paper presents the design, prototyping, modeling, and experimental evaluation of a VS and VD magnetorheological (MR) vehicle suspension system. It was first characterized by an INSTRON machine. Then, a phenomenological model was proposed to capture the characteristics of the damper and TS fuzzy approach was used to model the quarter car system where the proposed damper was installed. Different controllers, including skyhook, short-time Fourier transform and state observer based controller were designed to control the damper. Experimental results demonstrate that the quarter car system with the VS and VD suspension performs best in terms of reducing the sprung mass accelerations comparing with other suspensions.

Using positron emission markers for the localization of a robotic capsule endoscope is promising because it does not require onboard space or built-in battery for operation. Further, its compatibility with magnetic actuation is another significant advantage compared with conventional magnetic localization methods reported in the literature. In this paper, we propose a new tracking algorithm based on rigid-body transformation and gamma rays emitted from three positron emission markers onboard to localize an endoscopic capsule operating within the gastrointestinal tract of the human body. Different from traditional rigid-body transformation based on datasets of 3-D points, our method estimates the transformation parameters (e.g., translation vector and rotation angle) from several groups of 3-D lines in order to determine the locations of the markers emitting the gamma rays. Validated by both simulation data using a voxelized phantom in the Geant4 Application for Emission Tomography toolkit and the experimental data collected from a positron emission tomography scanner, the new localization method shows a significant improvement in the tracking accuracy (an average position error of 0.4 mm and orientation error of 1.9°) and the failure rate (18/9600 localization runs), compared to the localization results reported in the literature.

The studies on the autonomous electric vehicle are quite attractive due to fewer human-induced errors and improved safety in recent years. Extensive research has been done on the autonomous steering control of the mobile robot, but study on the on-road autonomous electric vehicle is still limited. This paper proposes a potential field method to achieve the trajectory control of the autonomous electric vehicle with in-wheel motors. Instead of strictly following a desired path, this method can form a steering corridor with a desired tracking error tolerance and the vehicle can be steered smoothly with less control effort. In this paper, the innovative potential filed function is presented first to determine the desired vehicle yaw angle. Then, according to this desired yaw angle, a two-level trajectory controller is proposed to achieve the trajectory control. Simulation results are shown to prove that this suggested trajectory controller can successfully control the vehicle to move within the desired road boundary and improve the handling and stability performance of the vehicle.

Robotic systems consisting of rigid elements connected to each other with single degree of freedom joints have been studied extensively. Robotic systems made of soft and smart materials are expected to provide a high dexterity and adaptability to their physical environment, like their biological counterparts. Electroactive polymer (EAP) actuators, also known as artificial muscles, which can operate both in wet and in dry environments with their promising features such as a low foot-print in activation and energy consumption, suitability to miniaturization, noiseless, and fully compliant operation can be employed to articulate a soft robotic system. This paper reports on kinematic modeling of a polypyrrole-based EAP actuator which is designed and fabricated to form helical configurations in 3-D from its initially spiral 2-D configuration. Denavit-Hartenberg transformations are combined with the backbone model of the actuator to establish the kinematic model. A parametric model has then been incorporated into the kinematic model to accurately estimate the helical configurations of the EAP actuator as a function of time under an electrical input. Experimental and simulation results, which are in good correlation, suggest that the proposed modeling approach is effective enough to estimate the 3-D helical configurations of the EAP actuator.

Accurate vehicle longitudinal velocity estimation is important for wheel slip ratio control in antilock braking systems. To overcome the problem of nonlinear tyre-road friction characteristic when designing an observer for velocity estimation, this paper presents a novel approach by using the model-based fuzzy technique. The nonlinear vehicle braking system is modelled by a Takagi-Sugeno fuzzy model first. A fuzzy observer is then constructed by using the available measurements of wheel angular velocity and braking torque with the estimated premise variables. All the possible disturbances and uncertainties are considered so that the designed observer is robust under an Hoo performance index from the disturbances to the estimation error. The design of the observer is achieved by solving a set of linear matrix inequalities. Numerical simulations on a quarter-vehicle braking model are used to validate the effectiveness of the proposed approach.

Vehicle side slip angle is a critical variable used in car safety systems like Electronic Stability Control. Due to the practical difficulty in direct measurement of side slip angle, accurate estimation of vehicle side slip angle using available signals is becoming important. This paper presents a novel algorithm for estimating the side slip angle of a vehicle in real time using inertial motion sensors. The algorithm uses a J48 decision tree classifier to assist the Extended Kaiman Filter (EKF) predictions of the vehicle side slip angle. The decision tree classifies the inertial data into classes based on the condition the slip angle is expected to be in. Using the class information asserted by the classifier, the error covariance parameter of the EKF is adjusted to compensate for changes in disturbances and nonlinearities. The results show that the decision tree assisted EKF technique presented in this paper is capable of predicting the slip angle with sound accuracy using inertial motion data.

The wireless capsule endoscope (WCE) is a first-line medical tool for the diagnosis of many gastrointestinal (GI) tract diseases such as obscure GI bleeding, Crohn’s disease, small bowel tumors, and Celiac disease. Over the past few years, significant research attention has been paid to upgrading the WCE from a diagnostic-only tool to an active medical robot having not only diagnostic capabilities but therapeutic functionalities, such as biopsy, microsurgery, and targeted drug delivery, as well. One of the major limitations that impedes the development of such a robotic-type endoscope is the lack of a highly accurate localization system. In this paper, we present an experimental evaluation of a new real-time localization method (patent pending) based on tracking three positron emission markers embedded in the cover of an endoscopic capsule. Coincidence gamma rays emitted from the markers are detected by surrounding gamma ray detectors. The position and orientation information of the capsule can then be extracted by an effective tracking algorithm. The experiments were conducted in two different commercial positron emission technology (PET) scanners: Philips Allegro and Philips TF64. The experimental results show that the proposed localization method could provide less than 0.5-mm position error and$\hbox{2.4}^{\circ }$orientation error in a localization time interval of 50 ms with an average computational time of 6 ms per time interval. Zero power consumption and zero space occupation inside the capsule are additional advantages of this localization method.