Image compression is increasingly important in applications like intelligent driving and smart surveillance systems. This study presents a novel cross view capture distributed image compression network (CVCDIC) to improve the compression quality by using decoder side information. The CVCDIC’s decoder utilizes feature extraction networks to extract features from both the primary image and the side information. Furthermore, a multi-level cross view attention module is designed to capture interrelated details between images at multiple hierarchical levels. Finally, a spatial refinement module, constructed on the foundation of information distillation networks, is designed to further refine the quality of reconstructed images. The results show that CVCDIC can achieve an MS-SSIM of 0.978 at 0.15 bpp, surpassing DSIN (0.925), NDIC (0.956), and ATN (0.955) on the KITTI Stereo dataset.

This paper proposes an underwater target perception framework to comprehensively explore target information in underwater scenes, to improve the work efficiency and safety of underwater operations. This framework adopts a layered processing mechanism including water column imaging, constant false alarm rate detection (CFAR) detection, and local feature analysis, to accurately distinguish between false targets, static targets, and dynamic targets in the underwater scene, and obtain the motion trajectory of dynamic targets. The experiment is designed to simulate the underwater operation scene, and the results prove the effectiveness of the proposed framework.

The convolutional neural network (CNN) has emerged as a powerful tool for decoding electroencephalogram (EEG), which owns the potential use in the event-related potential-based brain-computer interface (ERP-BCI). However, the intra-individual difference of ERP makes the traditional learning models trained on static EEG data hard to decode when the EEG features vary along the time, which limits the long-time performance of the model. Addressing this problem, this study proposes a three-dimension CNN (3D-CNN)-based model to decode the ERPs dynamically. As input, the EEG is transformed into a brain topographic map stream along time. Then the 3D-CNN applies three-dimension kernels to capture the dynamical characteristic of spatial feature at several time points. Ten subjects participated in a cross-time task for 6 or 12[Formula: see text]h. The 3D-CNN shows higher accuracies and shorter computational cost than the baseline models of the 2D-CNN, the long short term memory (LSTM), the back propagation (BP), and the fisher linear discriminant analysis (FLDA) when detecting the ERPs. In addition, four schemes of the 3D-CNN are compared to explore the influence of the structure on the performance. This result demonstrates advanced robustness of the 3D-CNN kernel to the intra-individual EEG difference, helping to launch a more practical EEG decoding model for a long-time use.

In the growing elderly population globally, patients with severe movement disorders account for a large proportion. Moreover, the development of intelligent service equipment can better assist them in their daily. This paper proposes a new service robot control system. The brain-computer interface (BCI) based on Steady-State Visual Evoked Potentials (SSVEP) is used to acquire and process electroencephalogram(EEG) signals and output various control commands accordingly. Then, considering the visual fatigue of SSVEP-BCI, we added an object detection method based on Yolov3-tiny and saliency prediction to identify the patient’s selection intention intelligently. The results show that the subject can successfully complete the object delivery task with an average accuracy of 90.3%. The proposed control system can help the patients control a service robot in a more intelligent and friendly way to realize some daily tasks.

Brain-Machine Interface (BMI) has emerged as a powerful tool for assisting disabled people and for augmenting human performance. Up so far, no studies have succeeded in the power augmentation for the multi-DOFs robot based on EEG signals, especially for the complex shoulder joint. In this work, we propose an electromyography (EMG) estimation method based on electroencephalography (EEG) signals to realize the power assistance. The positions of the electrodes where the motion information of shoulder joint is effectively and exactly extracted are discussed, and a linear model that correlates the EMG to the EEG signal is constructed utilizing motion-related features extracted from multi-location EEG measurements. The constructed model is used to estimate the human muscular activity of shoulder joint from EEG using Principal Component Analysis (PCA) method. The proposed approach is experimentally verified, and an average correlation coefficients are as high as about 0.90 for different subjects are obtained between the estimated and the actually measured EMG signal. Our results suggest that the estimation of EMG based on EEG is feasible. This demonstrates the potential of using EEG signals to support human activities via brain-machine interface.

Brain-computer interface (BCI) systems can translate the human mind into control commands, which makes it feasible to improve the life quality of physically challenged people. However, in real-life situations, it is still difficult for users to utilize robots to provide basic services with BCI systems. We aimed to propose a BCI-based system with a visual servo module to operate a service robot. We recorded single-channel steady-state visual evoked potentials (SSVEP) as input signals for the BCI system of this study. The visual stimuli for inducing SSVEP were modulated at seven different frequencies with the sampled sinusoidal method. Correspondingly, this SSVEP-based BCI system can generate seven control commands for the operation of the service robot, which can provide three fundamental services: mobility, manipulation, and delivery. The visual servo module was established to reduce the burden of users and accelerate service procedures. To evaluate the performance of this system, subjects were recruited to participate in the experiments. All the participants succeed in operating the robot to provide the basic services. According to the experimental results, this SSVEP-based BCI system that incorporates the visual servo module can be effectively used to operate service robots with reduced number of channels and increased ability to perform multiple tasks.

The traction force of any moving object on a floor is the frictional force between the object and the floor. Therefore, investigation of the frictional constraint is very important for such moving object on a floor as well as a biped walking robot. Conventionally, the study of frictional constraint of a biped robot was only constrained in translational slip, without considering rotational slip, which is important in actual biped walking. This is one of the causes the walking speed of biped robots lower than the one of human beings. In this paper, an evaluation method of frictional constraints for biped robots is proposed. The translational friction force and the twist frictional torque acting on the biped robot's sole are both deduced and verified for single support phase. First, based on a biped robot model, an approach to calculate the frictional force and torque is deduced as the quantitative expression of frictional constraint. Then, the experimental verification method is designed and the proposed calculation approach is confirmed. Finally, the consideration of the result of experiments is discussed. This approach is expected to be used for helping the realization of fast biped walkings.

the principle and control method of torso orientation in dynamic biped walking is addressed. First, the role of torso motion in walking performance, especially at the landing moment, is investigated by using a simplified model. Second, a control strategy of torso orientation is proposed, including the algorithms in the push-off and the single support phases (SSP). Furthermore, the module of torso control is integrated into the control method of dynamic biped walking, which was proposed in our previous research. The paper ends with the simulation of a humanoid robot with 21 internal degrees of freedom (DOF), and shows the feasibility of the proposed method.

Series elastic actuator (SEA) has been widely used in rehabilitation robotics, where human-robot interaction is required. Due to its intrinsic compliance, SEA can improve the usage of power for its motor, which leads to a compact and lightweight SEA design. The aim of this paper is to reduce the energy consumption and the power requirements of the motor of the SEA by optimizing the stiffness of its spring. This study is inspired by the biomechanics of a human ankle joint, which stores elastic energy during the first phases of the walking process and releases the stored energy in the next gait phases to propel the human body forward. Power analysis and optimization procedure are conducted on complete SEA models with different complexity, including inertia, damping and stiffness, and with open loop and closed loop control strategies. Simulation results demonstrate that a reduction of 56.6% of the peak motor power can be achieved with the optimized spring stiffness.

We are developing an intelligent compact and modular knee-ankle-foot robot gait rehabilitation at outpatient and home settings. The robot is designed with a novel compact compliant force controllable actuator. We adopt a modular design for the knee and ankle joint so that the robot can assist patients with different conditions of gait impairments. The light-weight anthropomorphic structure designed based on biomechanical studies is built with advanced composite materials to achieve portability. A prototype of the robot has been built for testing. In this paper, we present the mechanical design of the robot with focus on the actuator and mechanism design and analysis, with testing results to demonstrate the performance of the actuator.

Although a cellular manufacturing system can produce diversified products flexibly, its assembly efficiency is mainly limited by the operators' assembly performance. To improve the operators' assembly performance without longtime training, an assembly-skill-transferring method was proposed to realize the assembly skill transfer process from skilled operators to novice operators. In the cellular manufacturing system, the assembly skills consist of cognition skills and motor skills. Taking a “Peg-in-Hole Task” as an example, the effect of the proposed assembly transfer method was verified for motor skills. The results show that the proposed assembly-skill-transferring system can improve the novice operators' assembly performance greatly.

High-speed digital imaging (HSDI) of the larynx provides important information on the vocal fold vibrations that are closely associated with voice condition. We present an active contour (snake)-based algorithm for the automatic delineation of the glottis within image sequences captured from an HSDI system. The algorithm has three steps: first, a rough segmentation is performed by global thresholding, and followed by the detection of an ellipse-shaped region that approximates the glottal geometry, secondly, parameters of the ellipse are estimated using the principal component analysis (PCA) method and thirdly, the snake-method is applied using the estimated ellipse as an initial contour. The performance of the proposed approach is demonstrated through the use of clinical samples of the HSDI recordings obtained from subjects having both normal and pathological voice conditions. Finally the proposed method is compared with existing snake-based methods in terms of efficiency and segmentation accuracy.

Up to now, many robotic aids for the elderly's walking support or the disabled's walking rehabilitation are reported, and numerous electrical-powered wheelchairs are developed. In this paper, a new kind of omnidirectional wheelchair typed robot is developed. The robot not only can accomplish the walking support or walking rehabilitation as the elderly or the disabled walk, but also can realize the power assistance for a caregiver when he/she pushes the robot to move. The basic structure of the robot is described, and the omnidirectional mobility of the robot is analyzed. Further, an admittance based human-machine interaction controller is introduced for power assistance. Experiments are implemented, and the experimental results show that the pushing force can be reduced and well controlled arbitrarily as designed. The development purposes of the robot for walking support and power assistance are achieved.

In this paper, a new type of omnidirectional mobile robot is developed, that not only can be used for the elderly's walking support, the disabled's walking rehabilitation, but also can be used for a caregiver's power assistance when he/she pushes the robot to move while the elderly or the disabled is sitting in the seat. The omnidirectional mobility of the robot is analyzed, and an admittance based human-machine interaction controller is introduced for power assistance. The experimental results show that the pushing force is reduced and well controlled as we planned. The purpose of walking support and power assistance is achieved.

In this paper, a new considering approach for frictional constraint is proposed. It is pointed out that the conventional method only considering translational friction is not appropriate for biped walking, and the frictional rotation constraint should be taken into account. It is stressed that the translational motion and rotational motion will occur simultaneously when slip starts in biped walking, and the frictional rotation constraint plays a more important role than the frictional translation constraint. Furthermore, a method to calculate the slipping frictional force and torque, to predict the most possible slipping direction of a support foot in single support phase is developed. With this method, the quantitative compensation for the yaw-axis moment gets possible. Finally, a new frictional constraint rule applicable for any frictional motions is suggested

In this paper, a new design approach of bipedal walking pattern based on the synchronization of the motions in sagittal and lateral planes are presented and two walking patterns of ZMP fixed and ZMP variable cases are developed. Based on our previous work, bipedal walking is separated into the initial acceleration, double support, deceleration, and acceleration phases; consequently, the nature that bipedal walking is in fact a continuous acceleration and deceleration motion is revealed. With the discusses on the motions both in the sagittal and lateral planes, the fact that the motions in these two planes are tightly coupled together is clarified. The motion parameters such as the walking velocity, walking time, and phase stride can be easily changed simply by altering the swinging amplitude in lateral plane that is determined by the double support phase. The constraint conditions of the phase stride, velocity and swinging amplitude are investigated. Therefore, an approach for adjusting walking velocity by controlling the swinging amplitude is naturally developed. The motion planning is also presented and a numerical example is given out.

Walking principle of biped robot is clarified with ZMP (zero moment point) concept, friction constraint, and inverted pendulum model in this paper. The stable walking condition is derived out with ZMP constraint, friction constraint, and inverted pendulum model. With this result, the nature that the biped walking is in fact a continually acceleration and deceleration motion is presented and its walking velocity can be adjusted by controlling landing point. The desired ZMP trajectory is given out based on derived stable walking condition, and the motion parameters of biped robot, such as walking stride, period, and their restriction are investigated. The approach used in this paper is expected to extend to analyze the motion of running and jump robot.

This paper mainly discusses the characteristics of free sliding motion in releasing manipulation. Releasing manipulation is such a manipulation, in which an object is given initial velocities by striking and slides on a surface to stop at the pre-assigned destination under the action of friction. After the properties of releasing manipulation are given out based on our previous work, the trajectory, the relationship of the sliding motion and the object shape, and the motion monotonicity of any one object with arbitrary shape are intensively discussed. Several important results are originally obtained. Finally, a simple and effectual approach of determining the necessary initial velocity of the object is presented with the above obtained results.

The properties of releasing manipulation are given. To improve the precision of object posture and decrease trial numbers, two iterative learning control schemes, learning control based on convergent condition (LCBCC), and learning control based on optimal principle (LCBOP) are designed in an experiment-oriented way. These two methods are based on a linearized model. The experimental results show that these methods are effective. After discussing the characteristics of these control methods, we postulate that in the case of where the system does not have enough knowledge, LCBCC is the only choice and to learn system knowledge, after enough experience has been acquired, LCBOP is better than LCBCC, form the view point of convergence rate and precision.

In this paper, first we propose a new kind of manipulation-releasing manipulation, that is, by some means, the manipulator gives the object initial translational and rotational velocities to make the object slide on a surface by its inertia under the action of friction. Second, we give the basic equations and analyze the motion of the object. Third, based on numerical calculation, we summarize the motion characteristics, especially for a rectangle. Finally, we perform experiments to verify the validity of this manipulation.