Most target grabbing problems have been dealt with by computer vision system, however, computer vision method is not always enough when it comes to the precision contact grabbing problems during the teleoperation process, and need to be combined with the stiffness display to provide more effective information to the operator on the remote side. Therefore, in this paper a more portable stiffness display device with a small volume and extended function is developed based on our previous work. A new static load calibration of the improved stiffness display device is performed to detect its accuracy, and the relationship between the stiffness and the position is given. An effective target grabbing strategy is presented to help operator on the remote side to judge and control and the target is classified by multi-class SVM(supporter vector machine). The teleoperation system is established to test and verify the feasibility. A special experiment is designed and the results demonstrate that the improved stiffness display device could greatly help operator on the remote side control the telerobot to grab target and the target grabbing strategy is effective.
An amplify-and-forward(AF) dual-hop relay is proposed for secure communication within Wyner s wiretap channel.Based on an information-theoretic formulation,the average secrecy rate is characterized when two legitimate partners communicate over a quasi-static fading channel.Theoretical analysis and simulation results show that both cooperative strategies of average power scaling(APS) and instantaneous power scaling(IPS) are proved to be able to achieve information-theoretic security,and eavesdropper is unable to decode any information.
Study results in the last decades show that amount and quality of physical exercises,then the active participation,and now the cognitive involvement of patient in rehabilitation training are crucial to enhance recovery outcome of motor dysfunction patients after stroke.Rehabilitation robots mainly have been developed along this direction to satisfy requirements of recovery therapy,or focused on one or more of the above three points.Therefore,rehabilitation robot based on neuro-machine interaction has been proposed for the paralyzed limb training of post-stroke patient,which utilizes motor related EEG,UCSDI(Ultrasound Current Source Density Imaging),EMG for the robot control and feeds back the multi-sensory interaction information such as visual,auditory,force,haptic sensation to the patient simultaneously.This neuro-controlled and perceptual rehabilitation robot will bring great benefits to post-stroke patients.In order to develop such a kind of rehabilitation robot,some key technologies,such as non-invasive precise measurement and decoding of neural signals,realistic sensation feedback,coordinated control for both the rehabilitation robot and the patient,need to be solved.In this paper,some fundamental problems in developing and optimizing such a kind of rehabilitation robot based on neuro-machine interaction are proposed and discussed.
