Design, Modeling, and Control of a Single Leg for a Legged-Wheeled Locomotion System with Non-Rigid Joint

This article presents an innovative legged-wheeled system, designed to be applied in a hybrid robotic vehicle’s locomotion system, as its driving member. The proposed system will be capable to combine the advantages of legged and wheeled locomotion systems, having 3DOF connected through a combination of both rigid and non-rigid joints. This configuration provides the vehicle the ability to absorb impacts and selected external disturbances. A state space approach was adopted to control the joints, increasing the system’s stability and adaptability. Throughout this article, the entire design process of this robotic system will be presented, as well as its modeling and control. The proposed system’s design is biologically inspired, having as reference the human leg, resulting in the development of a prototype. The results of the testing process with the proposed prototype are also presented. This system was designed to be modular, low-cost, and to increase the autonomy of typical autonomous legged-wheeled locomotion systems.This work is financed by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia, within project UIDB/50014/2020.info:eu-repo/semantics/publishedVersio

Active interaction control of a rehabilitation robot based on motion recognition and adaptive impedance control

Although electromyography (EMG) signals and interaction force have been widely used in patient cooperative or interactive training, the conventional EMG based control usually breaks the process into a patient-driven phase and a separate passive phase, which is not desirable. In this research, an active interaction controller based on motion recognition and adaptive impedance control is proposed and implemented on a six-DOFs parallel robot for lower limb rehabilitation. The root mean square (RMS) features of EMG signals integrating with the support vector machine (SVM) classifier were used to online predict the lower limb intention in advance and to trigger the robot assistance. The impedance control strategy was adopted to directly influence the robot assistance velocity and allow the exercise to follow a physiological trajectory. Moreover, an adaptive scheme learned the muscle activity level in real time and adapted the robot impedance in accordance with patient's voluntary participation efforts. Experimental results on several healthy subjects demonstrated that the lower limb motion intention can be precisely predicted in advance, and the robot assistance mode was also adjustable based on human-robot interaction and muscle activity level of subjects. Comparing with the conventional EMG-triggered assistance methods, such a strategy can increase patient's motivation because the subject's movement intention, active efforts as well as the muscle activity level changes can be directly reflected in the trajectory pattern and the robot assistance speeds

Active interaction control applied to a lower limb rehabilitation robot by using EMG recognition and impedance model

Purpose – The purpose of this paper is to propose a seamless active interaction control method integrating electromyography (EMG)-triggered assistance and the adaptive impedance control scheme for parallel robot-assisted lower limb rehabilitation and training. Design/methodology/approach – An active interaction control strategy based on EMG motion recognition and adaptive impedance model is implemented on a six-degrees of freedom parallel robot for lower limb rehabilitation. The autoregressive coefficients of EMG signals integrating with a support vector machine classifier are utilized to predict the movement intention and trigger the robot assistance. An adaptive impedance controller is adopted to influence the robot velocity during the exercise, and in the meantime, the user’s muscle activity level is evaluated online and the robot impedance is adapted in accordance with the recovery conditions. Findings – Experiments on healthy subjects demonstrated that the proposed method was able to drive the robot according to the user’s intention, and the robot impedance can be updated with the muscle conditions. Within the movement sessions, there was a distinct increase in the muscle activity levels for all subjects with the active mode in comparison to the EMG-triggered mode. Originality/value – Both users’ movement intention and voluntary participation are considered, not only triggering the robot when people attempt to move but also changing the robot movement in accordance with user’s efforts. The impedance model here responds directly to velocity changes, and thus allows the exercise along a physiological trajectory. Moreover, the muscle activity level depends on both the normalized EMG signals and the weight coefficients of involved muscles