Emission source microscopy (ESM) technique can be utilized for the localization of electromagnetic interference (EMI) sources in electronic systems, its performance greatly depends on the scanner accuracy and back-propagation method. In this paper, we introduce a novel spherical ESM system driven by 6-DOF manipulator, and investigate the back-propagation based on sphere wave expansion and robot control strategy. For spherical scanning aperture, we fuse the robot kinematics model and measurement constraints, and propose solving the optimal scanning grid with nonlinear programming method. For manipulator control, we present a graph-based learning framework (Gash-LKH) that combines sparse graph neural network with Lin-Kernighan heuristic (LKH) solver. This framework adopts the gated single-head attention module and parallel sparse graph feature abstracting channels, it can produce high-qualified edge candidate set that help subsequent LKH solver generate optimal scanning path with lower memory cost and less computation. Extensive experiments are conducted to validate the performance of Gash-LKH and Spherical ESM system, the results have demonstrated the feasibility and superiority of spherical ESM system in providing accurate microscopy and localization in EMI measurement.Note to Practitioners—The motivation of this paper is to develop an automated ESM system that realizes the spherical aperture scanning and pattern reconstruction of radiation source. Since adoption the back-propagation method based on sphere wave expansion, this system is supposed to achieve better microscopy performance and lower truncation error than other scanner. In this paper, we employ 6-DOF manipulator as scanner, and propose a complete spherical aperture generation method that produces the discrete and even scanning grid based on any source, frequency band and measurement constraints. Furthermore, we propose an end-to-end learning framework, Gash-LKH, to solve the optimal scanning path for given scanning aperture. The achieved accuracy and time-consumption of Gash-LKH is satisfactory for solving large-scaled and high-density scanning path planning. Note that the entire framework can be trained through random 3D-TSP instance dataset, and can be transferred to handle various radiation sources operating in microwave band. We have demonstrated the feasibility of proposed methodology and system through the experiments using Elfin-5 manipulator and benchmark sources. The results have offered the possibilities of achieving satisfactory localization and characterization in EMI measurement.

In an era marked by the rapid advancement of information technology, individuals now have the ability to enhance their organic bodies with mechanical and computational devices, revolutionizing their capabilities in everyday activities. However, the seamless integration of biosignals with mechanical counterparts continues to pose significant challenges. To address this, fuzzy logic (FL) emerges as a potential key to these challenges, offering a promising solution for handling the uncertainties and ambiguity inherent in biosignals. It thereby facilitates improved cyborg intelligence in an array of applications, including but not limited to wireless body area networks, brain–computer interfaces, prosthetics, and exoskeletons. Although previous works have highlighted the enhancement of cyborg intelligence using fuzzy sets, all of them only dived single aspects of the cyborg intelligence enhancement applications, leading to a lack of comprehensive understanding. Therefore, we provide a holistic overview of the state-of-the-art applications of FL in cyborg enhancement technology, encompassing its benefits, challenges, and potential directions for future research.

The increasingly pervasive usage of robotic surgery not only calls for advances in clinical application but also implies high availability for preliminary medical education using virtual reality. Virtual reality is currently upgrading medical education by presenting complicated medical information in an immersive and interactive way. A system that allows multiple users to observe and operate via simulated surgical platforms using wearable devices has become an efficient solution for teaching where a real surgical platform is not available. This article developed a bioinspired virtual reality toolkit (BioVRbot) for education and training in robot-assisted minimally invasive surgery. It allows multiple users to manipulate the robots working on cooperative virtual surgery using bioinspired control. The virtual reality scenario is implemented using unity and can be observed with independent virtual reality headsets. A MATLAB server is designed to manage robot motion planning of incremental teleoperation compliance with the remote center of motion constraints. Wearable sensorized gloves are adopted for continuous control of the tooltip and the gripper. Finally, the practical use of the developed surgical virtual system is demonstrated with cooperative operation tasks. It could be further spread into the classroom for preliminary education of robot-assisted surgery for early-stage medical students.

Meta-reinforcement learning enables artificial agents to learn from related training tasks and adapt to new tasks efficiently with minimal interaction data. However, most existing research is still limited to narrow task distributions that are parametric and stationary, and does not consider out-of-distribution tasks during the evaluation, thus, restricting its application. In this paper, we propose MoSS, a context-based Meta-reinforcement learning algorithm based on Self-Supervised task representation learning to address this challenge. We extend meta-RL to broad non-parametric task distributions which have never been explored before, and also achieve state-of-the-art results in non-stationary and out-of-distribution tasks. Specifically, MoSS consists of a task inference module and a policy module. We utilize the Gaussian mixture model for task representation to imitate the parametric and non-parametric task variations. Additionally, our online adaptation strategy enables the agent to react at the first sight of a task change, thus being applicable in non-stationary tasks. MoSS also exhibits strong generalization robustness in out-of-distributions tasks which benefits from the reliable and robust task representation. The policy is built on top of an off-policy RL algorithm and the entire network is trained completely off-policy to ensure high sample efficiency. On MuJoCo and Meta-World benchmarks, MoSS outperforms prior works in terms of asymptotic performance, sample efficiency (3-50x faster), adaptation efficiency, and generalization robustness on broad and diverse task distributions.

Playing games between humans and robots have become a widespread human-robot confrontation (HRC) application. Although many approaches were proposed to enhance the tracking accuracy by combining different information, the problems of the intelligence degree of the robot and the anti-interference ability of the motion capture system still need to be solved. In this paper, we present an adaptive reinforcement learning (RL) based multimodal data fusion (AdaRL-MDF) framework teaching the robot hand to play Rock-Paper-Scissors (RPS) game with humans. It includes an adaptive learning mechanism to update the ensemble classifier, an RL model providing intellectual wisdom to the robot, and a multimodal data fusion structure offering resistance to interference. The corresponding experiments prove the mentioned functions of the AdaRL-MDF model. The comparison accuracy and computational time show the high performance of the ensemble model by combining k-nearest neighbor (k-NN) and deep convolutional neural network (DCNN). In addition, the depth vision-based k-NN classifier obtains a 100% identification accuracy so that the predicted gestures can be regarded as the real value. The demonstration illustrates the real possibility of HRC application. The theory involved in this model provides the possibility of developing HRC intelligence.

The exoskeleton is mainly used by subjects who suffer muscle injury to enhance motor ability in the daily life environment. Previous research seldom considers extending human collaboration skills to human-robot collaborations. In this article, two models, that is: 1) the following the better model and 2) the interpersonal goal integration model, are designed to facilitate the human-human collaborative manipulation in tracking a moving target. Integrated with dual-arm exoskeletons, these two models can enable the robot to successfully perform target tracking with two human partners. Specifically, the manipulation workspace of the human-exoskeleton system is divided into a human region and a robot region. In the human region, the human acts as the leader during cooperation, while, in the robot region, the robot takes the leading role. A novel region-based Barrier Lyapunov function (BLF) is then designed to handle the change of leader roles between the human and the robot and ensures the operation within the constrained human and robot regions when driving the dual-arm exoskeleton to track the moving target. The designed adaptive controller ensures the convergence of tracking errors in the presence of region switches. Experiments are performed on the dual-arm robotic exoskeleton for the subject with muscle damage or some degree of motor dysfunctions to evaluate the proposed controller in tracking a moving target, and the experimental results demonstrate the effectiveness of the developed control.

The papers in this special section focus on cyborg intelligence. Well-known scientists and experts have expressed concern that robots may take over the world. More generally, there is a concern that robots could take over human jobs and leave billions of people suffering long-term unemployment. Yet, such concerns ignored the potential of intelligence techniques to enhance the natural capabilities of human beings with in-the-body technologies and so become cyborgs with superior capabilities to robots. Cyborg intelligence is dedicated to improving the natural capabilities of human beings by integrating artificial intelligence (AI) with biological intelligence and in-the-body technologies through tight integrations of machines and biological beings.

The presence of unknown physical interaction between the patients’ body and surgical tool in laparoscopic surgery requires a secure end-effector positioning while assuring a reliable constraint motion. In this work, a task-space control approach based on fuzzy approximation is proposed for a teleoperated surgery scenario utilizing a serial redundant robot manipulator (7 degrees of freedom), the motions of which are constrained with respect to a point known as remote center of motion (RCM). The dynamical uncertainties due to the physical interaction are considered and estimated to maintain operational accuracy by introducing the decoupled adaptive fuzzy approximation technique. Experiments verify the presented approach’s effectiveness. Results indicate that with the proposed control method, the accuracy of the surgical operation is improved while the safety can be ensured for teleoperated surgery with RCM constraint.

In next-generation network architecture, the Cybertwin drove the sixth generation of cellular networks sixth-generation (6G) to play an active role in many applications, such as healthcare and computer vision. Although the previous sixth-generation (5G) network provides the concept of edge cloud and core cloud, the internal communication mechanism has not been explained with a specific application. This article introduces a possible Cybertwin based multimodal network (beyond 5G) for electrocardiogram (ECG) patterns monitoring during daily activity. This network paradigm consists of a cloud-centric network and several Cybertwin communication ends. The Cybertwin nodes combine support locator/identifier identification, data caching, behavior logger, and communications assistant in the edge cloud. The application focuses on monitoring the ECG patterns during daily activity because few studies analyze them under different motions. We present a novel deep convolutional neural network based human activity recognition classifier to enhance identification accuracy. The healthcare monitoring values and potential clinical medicine are provided by the Cybertwin based network for ECG patterns observing.

Recently, the human-like behavior on the anthropomorphic robot manipulator is increasingly accomplished by the kinematic model establishing the relationship of an anthropomorphic manipulator and human arm motions. Notably, the growth and broad availability of advanced data science techniques facilitate the imitation learning process in anthropomorphic robotics. However, the enormous dataset causes the labeling and prediction burden. In this article, the swivel motion reconstruction approach was applied to imitate human-like behavior using the kinematic mapping in robot redundancy. For the sake of efficient computing, a novel incremental learning framework that combines an incremental learning approach with a deep convolutional neural network is proposed for fast and efficient learning. The algorithm exploits a novel approach to detect changes from human motion data streaming and then evolve its hierarchical representation of features. The incremental learning process can fine-tune the deep network only when model drifts detection mechanisms are triggered. Finally, we experimentally demonstrated this neural network's learning procedure and translated the trained human-like model to manage the redundancy optimization control of an anthropomorphic robot manipulator (LWR4+, KUKA, Germany). This approach can hold the anthropomorphic kinematic structure-based redundant robots. The experimental results showed that our architecture could not only enhance the regression accuracy but also significantly reduce the processing time of learning human motion data.

How to enable robotic compliant manipulation has become a critical problem in the robotics field. Inspired by a biomimetic adaptive control strategy, this article presents a novel representation model named human-like compliant movement primitives (Hl-CMPs) which could allow a robot to learn human-like compliant behaviors. The state-of-the-art approaches can hardly learn complete compliant profiles for a specific task. Comparatively, our model can encode task-specific parametric movement trajectories, correspondingly associated with dynamic trajectories including both impedance and feedforward force profiles. The compliant profiles are learned based on a biomimetic control strategy derived from the human motor learning in the muscle space, enabling the robot to simultaneously learn the impedance and the force while executing the movement trajectories obtained from human demonstration. Furthermore, both the kinematic and the dynamic profiles are learned in the parametric space, thus enabling the representation of a skill using corresponding parameters (i.e, task-specific parameters). Hl-CMps can allow the robot to automatically learn compliant behaviors in an online manner after kinematic demonstration. Our approach is validated by an insertion task and a cutting task based on a KUKA LBR iiwa robot.

Many previous works of soft wearable exoskeletons (exosuit) target at improving the human locomotion assistance, without considering the impedance adaption to interact with the unpredictable dynamics and external environment, preferably outside the laboratory environments. This article proposes a novel hierarchical human-in-the-loop paradigm that aims to produce suitable assistance powers for cable-driven lower limb exosuits to aid the ankle joint in pushing off the ground. It includes two primary loop layers: impedance learning in the external loop and human-in-the-loop adaptive management in the inner loop. Considering unknown terrains, its impedance model can be transferred to a quadratic programming problem with specified constraints, which a designed primal-dual optimization prototype then solves. Then, the presented impedance learning strategy is introduced to regulate the impedance model with the adaptive assistant powers for humans on different terrains. An adaptive controller is designed in the inner loop to balance the nonlinearities and compliance existing in the human-exosuit coexistence, while the robust mechanism compensates for disturbances to facilitate trajectory management without employing the general regressor. The advantage of the proposed technique over conventional solutions with fixed impedance parameters is that it can improve human walking performance over different terrains. Experiments demonstrate the significance of the approach.

The upper-limb prosthesis has been extensively studied using electromyography (EMG) signals to overcome the physical and functional deficiencies of amputees in recent years. However, most studies focus on the discrete classification of gestures and ignore the interconnection between the classification results and the neuroprosthesis control interface, which plays a vital role in system development. In this article, a new continuous control scheme is proposed to achieve an effective control of the developed upper-limb prosthesis. It utilizes eight channels of EMG signals of the human upper limb to model and control the developed prosthesis. A continuous control scheme is proposed that combines the state of the system and the decoding results to dynamically produce the expected angular velocity of the joint based on the results of the classification. Finally, experiments are performed to demonstrate the effectiveness of the proposed algorithm using an upper-limb neuroprosthesis, achieving the reach and grasp tasks. The results showed that it improves performance with a regular angular velocity of the joint, which underlines the importance of an adequate control scheme for the EMG-guided prosthesis.

Accurate path tracking and stability are the main challenges of lateral motion control in mobile robots, especially under the situation with complex road conditions. The interaction force between robots and the external environment may cause interference, which should be considered to guarantee its path tracking performance in dynamic and uncertain environments. In this article, a flexible lateral control scheme is considered for the developed wheel-legged robot, which consists of a cubature Kalman algorithm to evaluate the centroid slip angle and the yaw rate. Furthermore, a fuzzy compensation and preview angle-enhanced sliding model controller to improve the tracking accuracy and robustness. Finally, some simulations and experimental demonstrations using the four-wheel-legged robot (BIT-NAZA) are carried out to illustrate the effectiveness and robustness, and the proposed method has achieved satisfactory results in high-precision trajectory tracking and stability control of the mobile robot.

Under-actuated bionic hands have achieved tremendous popularity in many fields because of their advantages of lightweight, budget-friendly, satisfactory flexibility, and adaptability. Except for the bionic mechanical design, various anthropomorphic control strategies have been proposed and investigated in the last decades. However, due to its under-actuated characteristic, there are still many challenges for anthropomorphic control of all the degrees of freedom (DOFs) using less input. It is challenging to map the human hand kinematic synergies on robotic hands, particularly for a dynamic environment. Therefore, it is worth studying how to control the under-actuated bionic hand effectively in a dynamic environment. In this paper, an anthropomorphic control method is proposed using sensor fusion of hand kinematic inputs to control the under-actuated bionic hand. In order to map the kinematics of human fingers to the bionic hand, a novel finger bending angle is defined to represent the posture of human fingers. Multiple Leap Motion Controllers (LMC) are fused to estimate the stable and accurate finger bending angles to avoid the occlusion problem. Finally, experiments with real-time control of the under-actuated bionic hand are implemented to demonstrate the proposed approach’s effectiveness.

Learning manipulation skills from open surgery provides more flexible access to the organ targets in the abdomen cavity and this could make the surgical robot working in a highly intelligent and friendly manner. Teaching by demonstration (TbD) is capable of transferring the manipulation skills from human to humanoid robots by employing active learning of multiple demonstrated tasks. This work aims to transfer motion skills from multiple human demonstrations in open surgery to robot manipulators in robot-assisted minimally invasive surgery (RA-MIS) by using TbD. However, the kinematic constraint should be respected during the performing of the learned skills by using a robot for minimally invasive surgery. In this article, we propose a novel methodology by integrating the cognitive learning techniques and the developed control techniques, allowing the robot to be highly intelligent to learn senior surgeons' skills and to perform the learned surgical operations in semiautonomous surgery in the future. Finally, experiments are performed to verify the efficiency of the proposed strategy, and the results demonstrate the ability of the system to transfer human manipulation skills to a robot in RA-MIS and also shows that the remote center of motion (RCM) constraint can be guaranteed simultaneously. Note to Practitioners-This article is inspired by limited access to the manipulation of laparoscopic surgery under a kinematic constraint at the point of incision. Current commercial surgical robots are mostly operated by teleoperation, which is representing less autonomy on surgery. Assisting and enhancing the surgeon's performance by increasing the autonomy of surgical robots has fundamental importance. The technique of teaching by demonstration (TbD) is capable of transferring the manipulation skills from human to humanoid robots by employing active learning of multiple demonstrated tasks. With the improved ability to interact with humans, such as flexibility and compliance, the new generation of serial robots becomes more and more popular in nonclinical research. Thus, advanced control strategies are required by integrating cognitive functions and learning techniques into the processes of surgical operation between robots, surgeon, and minimally invasive surgery (MIS). In this article, we propose a novel methodology to model the manipulation skill from multiple demonstrations and execute the learned operations in robot-assisted minimally invasive surgery (RA-MIS) by using a decoupled controller to respect the remote center of motion (RCM) constraint exploiting the redundancy of the robot. The developed control scheme has the following functionalities: 1) it enables the 3-D manipulation skill modeling after multiple demonstrations of the surgical tasks in open surgery by integrating dynamic time warping (DTW) and Gaussian mixture model (GMM)-based dynamic movement primitive (DMP) and 2) it maintains the RCM constraint in a smaller safe area while performing the learned operation in RA-MIS. The developed control strategy can also be potentially used in other industrial applications with a similar scenario.

The complexity of surgical operation can be released significantly if surgical robots can learn the manipulation skills by imitation from complex tasks demonstrations such as puncture, suturing, and knotting, etc.. This paper proposes a reinforcement learning algorithm based manipulation skill transferring technique for robot-assisted Minimally Invasive Surgery by Teaching by Demonstration. It employed Gaussian mixture model and Gaussian mixture Regression based dynamic movement primitive to model the high-dimensional human-like manipulation skill after multiple demonstrations. Furthermore, this approach fascinates the learning and trial phase performed offline, which reduces the risks and cost for the practical surgical operation. Finally, it is demonstrated by transferring manipulation skills for reaching and puncture using a KUKA LWR4+ robot in a lab setup environment. The results show the effectiveness of the proposed approach for modelling and learning of human manipulation skill.

In this paper, an Internet of Things-based human-robot collaborative control scheme is developed in Robot-assisted Minimally Invasive Surgery scenario. A hierarchical operational space formulation is designed to exploit the redundancies of the 7-DoFs redundant manipulator to handle multiple operational tasks based on their priority levels, such as guaranteeing a remote center of motion constraint and avoiding collision with a swivel motion without influencing the undergoing surgical operation. Furthermore, the concept of the Internet of Robotic Things is exploited to facilitate the best action of the robot in human-robot interaction. Instead of utilizing compliant swivel motion, HTC VIVE PRO controllers, used as the Internet of Things technology, is adopted to detect the collision. A virtual force is applied to the robot elbow, enabling a smooth swivel motion for human-robot interaction. The effectiveness of the proposed strategy is validated using experiments performed on a patient phantom in a lab setup environment, with a KUKA LWR4+ slave robot and a SIGMA 7 master manipulator. By comparison with previous works, the results show improved performances in terms of the accuracy of the RCM constraint and surgical tip.

In this paper, a bilateral teleoperation control of a serial robot manipulator, which guarantees a Remote Center of Motion (RCM) constraint in its kinematic level, is developed. A two-layered approach based on the energy tank model is proposed to achieve haptic feedback on the end effector with a pedal switch. The redundancy of the manipulator is exploited to maintain the RCM constraint using the decoupled Cartesian Admittance Control. Transparency and stability of the proposed bilateral teleoperation are demonstrated using a KUKA LWR4+ serial robot and a Sigma 7 haptic manipulator with an RCM constraint in augmented reality. The results prove that the control can achieve not only the bilateral teleoperation but also maintain the RCM constraint.

In this paper, an improved motion planning scheme is proposed for surgical robot control with multiple active constraints, including joint constraints, joint velocity constraints and remote center of motion constraints. It introduces an improved recurrent neural network (RNN) to optimize the online motion planning respect to multiple constraints. The demonstrated surgical operation trajectory is derived using teaching by demonstration. An improved motion planning scheme using the novel recurrent neural network is then designed to achieve the accurate task tracking under the multiple constraints. The general quadratic performance index is adopted to represent the constraints. Finally, the effectiveness of the proposed algorithm is demonstrated using KUKA LWR4+ robot in a lab setup environment.

For the time varying optimization problem, the tracking error cannot converge to zero at the finite time because of the optimal solution changing over time. This paper proposes a novel varying parameter recurrent neural network (VPRNN) based hierarchical optimization of a 7-DoF surgical manipulator for Robot-Assisted Minimally Invasive Surgery (RAMIS), which guarantees task tracking, Remote Center of Motion (RCM) and manipulability index optimization. A theoretically grounded hierarchical optimization framework based is introduced to control multiple tasks based on their priority. Finally, the effectiveness of the proposed control strategy is demonstrated with both simulation and experimental results. The results show that the proposed VPRNN-based method can optimal three tasks at the same time and have better performance than previous work.

Human activity recognition (HAR) using smartphones provides significant healthcare guidance for telemedicine and long-term treatment. Machine learning and deep learning (DL) techniques are widely utilized for the scientific study of the statistical models of human behaviors. However, the performance of existing HAR platforms is limited by complex physical activity. In this article, we proposed an adaptive recognition and real-time monitoring system for human activities (Ada-HAR), which is expected to identify more human motions in dynamic situations. The Ada-HAR framework introduces an unsupervised online learning algorithm that is independent of the number of class constraints. Furthermore, the adopted hierarchical clustering and classification algorithms label and classify 12 activities (five dynamics, six statics, and a series of transitions) autonomously. Finally, practical experiments have been performed to validate the effectiveness and robustness of the proposed algorithms. Compared with the methods mentioned in the literature, the results show that the DL-based classifier obtains a higher recognition rate (95.15%, waist, and 92.20%, pocket). The decision-tree-based classifier is the fastest method for modal evolution. Finally, the Ada-HAR system can monitor human activity in real time, regardless of the direction of the smartphone.

This paper proposes a manipulability optimization control of a 7-DoF robot manipulator for Robot-Assisted Minimally Invasive Surgery (RAMIS), which at the same time guarantees a Remote Center of Motion (RCM). The first degree of redundancy of the manipulator is used to achieve an RCM constraint, the second one is adopted for manipulability optimization. A hierarchical operational space formulation is introduced to integrate all the control components, including a Cartesian compliance control involving the main surgical task, a first null-space controller for the RCM constraint, and a second null-space controller for manipulability optimization. Experiments with virtual surgical tasks, in an augmented reality environment, were performed to validate the proposed control strategy using the KUKA LWR 4 +. The results demonstrate that end-effector accuracy and RCM constraint can be guaranteed, along with improving the manipulability of the surgical tip.

In this paper, a teleoperation control of a 7-DoF robot manipulator for Minimally Invasive Surgery (MIS), which guarantees a safety-enhanced compliant behavior in the null space, is described. The redundancy of the manipulator is exploited to provide a flexible workspace for nurses or other staff (assisting physicians, patient support). The issue with safety and accurate surgical task execution may arise in the presence of human-robot interaction. Based on the implemented impedance control of tele-operated MIS tasks, a safety enhanced constraint is applied on the compliant null space motion. At the same time, the control approach integrates an adaptive fuzzy compensator to guarantee the accuracy of the surgical tasks during the uncertain human-robot interaction. The performance of the proposed algorithm is verified with virtual surgical tasks. The results showed that the compliant null space motion is constrained in a safe area, and also that the accuracy of tool tip is improved, providing a flexible and safe collaborative behavior in the null space for human-robot interaction during surgical tasks.

With the emerging vehicular network and the possible diverse applications, Intelligent Transportation Systems (ITS) have been evolving to Cooperative ITS (C-ITS, CITS) with connected intelligent vehicles, and the topics in this domain also raise more and more research interesting recently. However, subjecting to the immaturity of V2X communication technology and the deployment of intelligent vehicles in large scale, such studies and the corresponding verification are all challenged within current situations. Focusing on several emerging features, such as cyber-physical fusion, vehicular networking, service-carrier etc, a new simulator QoS-CITS for such service-oriented C-ITS is designed. To enhance the adaptivity, an para-reconfigurable architecture is firstly adopted, within which all reservation-based models of traffic objects, state-driven behaviors, cooperation mechanisms, and policies proposed for service-oriented C-ITS are implemented. And then, a series of experiments are conducted via employing parameters within typical scenes, and all fundamental functions of QoS-CITS are verified. With this simulator, researchers can carry out various experiments, not only classic ones but service-oriented, via setting parameters according to their study and verification requirements, and then analyze the performance with statistics data automatically recorded.

As driverless vehicles are increasingly becoming possible, so does the use of such vehicles as intelligent carriers in different domains. Intelligent transportation systems (ITSs) show increasingly heterogeneous, cyber-physical, cooperative, and service-oriented features and are beginning to be merged with the emerging cyber-physical-social systems. Given this new trend, how to make these intelligent vehicles cooperate more safely and efficiently with one another according to novel constraints, such as mission type and quality-of-service (QoS), has become a vital aspect of cooperative ITS (C-ITS). With these emerging characteristics, the classical passing-through-intersection problem has gained new connotations, worth further exploring. After analyzing the essences of this new problem, service-oriented cooperation models and mechanisms for whole autonomous vehicles approaching intersections are investigated in this paper. First, related traffic objects and possible vehicular behaviors are abstracted and modeled with the cyber-physical cooperative features and QoS constraints. A new reservation-based scheduling procedure is then conducted by employing the concepts of vehicle-to-infrastructure communication, and typical vehicular passing-through behaviors and several spatial-temporal constraints are designed to coordinate vehicles passing through an intersection divided as a series of continuous static critical sections. Given these considerations, a priority-based centralized scheduling algorithm, named csPriorFIFO, which adopts a novel priority inheritance mechanism to promote the traffic QoS of emergent vehicles, is proposed. Finally, all these designs are implemented in a traffic simulator named QoS-CITS, and the functions and the performance of these studied methods are verified and compared.

This paper studies the optimal distribution of feet forces and control of multilegged robots with uncertainties in both kinematics and dynamics. First, a constrained dynamics for multilegged robots and the constrained environment model are established by considering both kinematic and dynamic uncertainties. Under an external wrench for multilegged robots, the foot forces and moments of the supporting legs can be formulated as quadratic programming problems subject to linear and nonlinear constraints. The neurodynamics of recurrent neural network is developed for foot force optimization. For the obtained optimized tip-point force and the motion of legs, we propose a hybrid task-space trajectory and force tracking based on fuzzy system and adaptive mechanism that are used to compensate for the external perturbation, kinematics, and dynamics uncertainties. The tracking of task-space trajectory and constraint force is achieved under unknown dynamical parameters, constraints, and disturbances. Extensive simulations have been provided to verify the effectiveness of the proposed scheme.

This paper presents fuzzy approximation-based adaptive backstepping control of an exoskeleton for human upper limbs to provide forearm movement assistance so that a human forearm can track any continuous desired trajectory (or constant setpoint) in the presence of parametric/functional uncertainties, unmodeled dynamics, actuator dynamics, and/or disturbances from environments. Given the desired trajectories of human forearm positions, in the developed control, adaptive fuzzy approximators are used to estimate the dynamical uncertainties of the human-robot system, and an iterative learning scheme is utilized to compensate for unknown time-varying periodic disturbances. With the synthesis of the backstepping, iterative learning, and Lyapunov function approaches, the developed controller does not require exact knowledge of the exoskeleton model, and the close-loop system can be proven to be semiglobally uniformly bounded. Three comparison experiments are conducted to illustrate the effectiveness of the proposed control scheme by tracking periodic/repeated trajectories.