736 publications from this institution
Miniature jumping robots (MJRs) have difficulty executing autonomous movements in unstructured environments with obstacles because of their limited perception and computing resources. This study investigates the obstacle detection and autonomous stair climbing methods for MJRs. We propose an obstacle detection method based on a combination of attitude and distance detections, as well as MJRs' motion. A MEMS inertial sensor collects the yaw angle of the robot, and a ranging sensor senses the distance between the robot and the obstacle to estimate the size of the obstacle. We also propose an autonomous stair climbing algorithm based on the obstacle detection method. The robot can detect the height and width of stairs and its position relative to the stairs and then repeatedly jump to climb them step by step. Moreover, the height, width, and position are sent to a control terminal through a wireless sensor network to update the information regarding the MJR and stairs in a control interface. Furthermore, we conduct stair detection, modeling, and stair climbing experiments on the MJR and obtain acceptable precisions for autonomous obstacle negotiation. Thus, the proposed obstacle detection and stair climbing methods can enhance the locomotion capability of the MJR in environmental monitoring, search and rescue, etc.
Barrier detection and tracking is a key technique in augmented reality(AR). By adding up the sense of objects, users are able to safely observe or avoid moving objects in the view. With the development of 3D Lidar technology, the acquisition of 3D points in the scene is getting more efficient. In comparison with image data, the 3D points from Lidar contain reliable depth information in a large range. However, processing unstructured point cloud is less efficient for augmented reality applications. By noticing the structure of the 3D points captured from Lidar, we propose to parameterize the data from Lidar. In this way, we are able to reuse detection and tracking methods from images for a simple solution in barrier detection and tracking from Lidar data. We test our method by using the Lidar data captured from an unmanned ship. The results show that our method can quickly detect the barriers with bounding boxes, indicating the distance, direction and size of the barrier in the scene.
Haptic devices with multi-finger input are highly desirable in providing realistic and natural feelings when interacting with the remote or virtual environment. Compared with the conventional actuators, MR (Magneto-rheological) actuators are preferable options in haptics because of larger passive torque and torque-volume ratios. Among the existing haptic MR actuators, most of them are still bulky and heavy. If they were smaller and lighter, they would become more suitable for haptics. In this paper, a small-scale yet powerful MR actuator was designed to build a multi-finger interface for the 6 DOF haptic device. The compact structure was achieved by adopting the multi-disc configuration. Based on this configuration, the MR actuator can generate the maximum torque of 480 N.mm with dimensions of only 36 mm diameter and 18 mm height. Performance evaluation showed that it can exhibit a relatively high dynamic range and good response characteristics when compared with some other haptic MR actuators. The multi-finger interface is equipped with three MR actuators and can provide up to 8 N passive force to the thumb, index and middle fingers, respectively. An application example was used to demonstrate the effectiveness and potential of this new MR actuator based interface.
Maximum scatter difference (MSD) has been widely used in face recognition for feature extraction. However, its advantage will decrease when each object has only one training sample because the intra-class variations cannot be statistically measured in this case. To address the problem, a novel method based on m-MSD and SVD is proposed in this paper. A facial image is decomposed by the SVD algorithm, so one image can be transformed into several approximate images by reconstructing method with different number of singular values. That is to say, the number of training sample for each object is increased by singular value decomposition algorithm. Thus, the MSD algorithm can be applied to extract the discriminant features. Experiment results based on FERET and ORL face database show that the proposed method is efficient and it can achieve higher recognition rate than several existing algorithms.
Feature extraction plays an important role in brain-computer interface (BCI) systems. In order to characterize the motor imagery related rhythm and higher-order statistics information contained within the EEG signals, a novel feature extraction method based on harmonic wavelet transform and bispectrum is developed and applied to the recognition of right and left motor imageries for developing EEG-based BCI systems. The experimental results on the Graz BCI data set have shown that the separability of the two classes features extracted by the proposed method is notable. Its performance was evaluated by a linear discriminant analysis (LDA) classifier. The recognition accuracy of 90% was obtained. The recognition results have demonstrated the effectiveness of the proposed method. This method provides an effective way for EEG feature extraction in BCI system.
In the paper, the concept of logical stochastic resonance is applied to implement logic operation and latch operation in time-delayed synthetic genetic networks derived from a bacteriophage λ. Clear logic operation and latch operation can be obtained when the network is tuned by modulated periodic force and time-delay. In contrast with the previous synthetic genetic networks based on logical stochastic resonance, the proposed system has two advantages. On one hand, adding modulated periodic force to the background noise can increase the length of the optimal noise plateau of obtaining desired logic response and make the system adapt to varying noise intensity. On the other hand, tuning time-delay can extend the optimal noise plateau to larger range. The result provides possible help for designing new genetic regulatory networks paradigm based on logical stochastic resonance.
In this paper, a novel stiffness display device based on deformable length of elastic element control is presented firstly. The stiffness display device is composed of a thin elastic beam and an actuator to adjust the length of the beam. By controlling the beam length, the stiffness display device can reproduce the stiffness of the virtual object from very soft to hard, so that the human can feel it through fingertip as if he directly touches the virtual object by interacting with the device. Then experiments of human fingertip perception by using this device are carried out. The aims of the experiments are to explore the relations between human perception and differential thresholds, and get the characteristics of human fingertip, including force, times, frequency. At last, some important conclusions are drawn. The results can aid in the process of designing haptic devices.
In this brief, based on Lyapunov-Krasovskii functional approach and appropriate integral inequality, a new sufficient condition is derived to guarantee the global stability for delayed neural networks with unbounded distributed delay, in which the improved delay-partitioning technique and general convex combination are employed. The LMI-based criterion heavily depends on both the upper and lower bounds on time delay and its derivative, which is different from the existent ones and has wider application fields than some present results. Finally, three numerical examples can illustrate the efficiency of the new method based on the reduced conservatism which can be achieved by thinning the delay interval.
Hand-controllers, as human-machine-interface (HMI) devices, can transfer the position information of the operator’s hands into the virtual environment to control the target objects or a real robot directly. At the same time, the haptic information from the virtual environment or the sensors on the real robot can be displayed to the operator. It helps human perceive haptic information more truly with feedback force. A parallel hand-controller is designed in this paper. It is simplified from the traditional delta haptic device. The swing arms in conventional delta devices are replaced with the slider rail modules. The base consists of two hexagons and several links. For the use of the linear sliding modules instead of swing arms, the arc movement is replaced by linear movement. So that, the calculating amount of the position positive solution and the force inverse solution is reduced for the simplification of the motion. The kinematics, static mechanics, and dynamic mechanics are analyzed in this paper. What is more, two demonstration applications are developed to verify the performance of the designed hand-controller.
Stroke is a leading cause of disability worldwide. In this paper, a novel robot-assisted rehabilitation system based on motor imagery electroencephalography (EEG) is developed for regular training of neurological rehabilitation for upper limb stroke patients. Firstly, three-dimensional animation was used to guide the patient image the upper limb movement and EEG signals were acquired by EEG amplifier. Secondly, eigenvectors were extracted by harmonic wavelet transform (HWT) and linear discriminant analysis (LDA) classifier was utilized to classify the pattern of the left and right upper limb motor imagery EEG signals. Finally, PC triggered the upper limb rehabilitation robot to perform motor therapy and gave the virtual feedback. Using this robot-assisted upper limb rehabilitation system, the patient's EEG of upper limb movement imagination is translated to control rehabilitation robot directly. Consequently, the proposed rehabilitation system can fully explore the patient's motivation and attention and directly facilitate upper limb post-stroke rehabilitation therapy. Experimental results on unimpaired participants were presented to demonstrate the feasibility of the rehabilitation system. Combining robot-assisted training with motor imagery-based BCI will make future rehabilitation therapy more effective. Clinical testing is still required for further proving this assumption.
