Least Squares Support Vector Machine Kalman Filter for Physiological Tremor Suppression in Minimally Invasive Surgical Robot

This paper studies the physiological tremor filtering in minimally invasive surgical robot.The surgeon􀆳s physiological tremor of the hand can cause the vibration of the tip of the surgical instrument,which may reduce operative accuracy and limit the application of surgical robots.Aiming at the vibration caused by physiological tremor of hand,we propose a Least Squares Support Vector Machine Kalman Filter(LSSVMKF),which can filter the tremor by estimating and modeling the tremor signal by Kalman filter and then superimposing it reversely in the control signal.When estimating and modeling the tremor signal,the filter uses the Least Squares Support Vector Machine(LS⁃SVM)to build the regression model of the constant parameters(Process Noise Covariance and Measurement Noise Covariance)of the traditional Kalman filter,which can dynamically adjust these parameters during the operation and improve the accuracy of Kalman filter.The simulation results show that the LSSVMKF can effectively filter out the tremor signal,thereby improving the accuracy of surgery.

Development and Analysis of an Electrically Actuated Lower Extremity Assistive Exoskeleton

An electrically actuated lower extremity exoskeleton is developed, in which only the knee joint is actuated actively while other joints linked by elastic elements are actuated passively. This paper describes the critical design criteria and presents the process of design and calculation of the actuation system. A flexible physical Human-Robot-Interaction （pHR1） measurement device is designed and applied to detect the human movement, which comprises two force sensors and two gasbags attached to the inner surface of the connection cuff. An online adaptive pHRI minimization control strategy is proposed and implemented to drive the robotic exoskelcton system to follow the motion trajectory of human limb. The measured pHRI information is fused by the Variance Weighted Average （VWA） method. The Mean Square Values （MSV） of pHRI and control torque are utilized to evaluate the performance of the exoskeleton. To improve the comfort level and reduce energy consumption, the gravity compensation is taken into consideration when the control law is designed. Finally, practical experiments are performed on healthy users. Experimental results show that the proposed system can assist people to walk and the outlined control strategy is valid and effective.

Locomotion Stability Analysis of Lower Extremity Augmentation Device

Stability is of great significance in the theoretical framework of biped locomotion.Real-time control and walking patterns planning are on the premise that the robot works in the stable condition.In this paper,we address the crucial issue of the locomotion stability based on the modified Poincare return map and the hybrid automata.Not akin to the traditional stability criteria,i.e.,the Zero Moment Point(ZMP)and the Center of Mass(CoM),the modified Poincare return map is more appropriate for both dynamic walking arid non-periodic walking.Moreover,a novel high-level reinforcement learning methodology,so-called active PI2 CMA-ES,is proposed in this paper to plan the exoskeleton locomotion.The proposed learning methodology demonstrates that the locomotion of the exoskeleton is asymptotically stable according to the modified Poincare return map criterion.Finally,the proposed learning methodology is tested by the Lower Extremity Augmentation Device(LEAD)and its effectiveness is verified by the experiments.