Traditional upper limb rehabilitation exercises are primarily aimed at regaining the strength or range of motion of the patients' injured area. An alternative option that has been presented in the last years is the use of haptic interfaces, which have shown their potential as tools that support rehabilitation therapies. This article presents a haptic system of rehabilitation for fine upper limb movements, whose main characteristic is that users of the system can interact in a visual and tactile fashion with virtual objects mixed with real scenarios, thereby achieving an augmented reality environment. The system was tested in two stages, both with subjects who had a degree of disability in upper limbs. The data collected were followed trajectories, follow-up errors and the muscular activity obtained by means of electromyography; the collected information enabled the analysis, in a quantitative way, of the degree of progress of the patients. In addition, the assessments made by physiotherapists were considered, concluding that the proposed system can be used as a viable complementary tool for conventional rehabilitation therapies.
In general, indirect force control schemes (stiffness, impedance, etc.) assume that robot actuators can provide any torque value to achieve the goal of interaction control. This study attempts to regulate robot–environment interaction by generating bounded control signals and to avoid accurate knowledge of the parameters associated with gravitational effects and the stiffness of the environment. To achieve this aim, a generalized and saturating adaptive stiffness control scheme in task‐space is proposed. For the purpose of this work, the interaction or contact between the end‐effector of a robot manipulator and the environment is modeled as a vector of bounded spring‐like forces. The proposed control approach has a proportional‐derivative structure with static model‐based compensation of gravitational and interaction forces, which it achieves by including a regressor‐based adaptive term. As a theoretical basis to support the proposal, Lyapunov's stability analysis of the closed‐loop equilibrium vector is presented. Finally, the suitability of the proposed stiffness control scheme for interaction tasks is verified through simulations and experimental tests by using three‐degree‐of‐freedom robotic arms.
In applications where robot manipulators are in contact with the environment, better known as constrained-motion applications, it is necessary to have a control algorithm that guarantees a suitable interaction between the robot and its environment. The main interaction control schemes in taskspace require accurate knowledge of the dynamics of the system to be controlled; then, if the parameters of the environment and the robotic system are unknown, it is essential to use adaptive control schemes. This paper presents an adaptive stiffness control scheme for robot manipulators, that allows to solve the problem of interaction control in the face of parametric uncertainty about the stiffness of the environment and the gravitational forces acting on the robot. The control structure is based on a regressor to estimate the unknown parameters and it is supported by a stability analysis, in the Lyapunov sense, to demonstrate the asymptotic stability of the equilibrium point of the closed-loop system. Finally, some results obtained in simulation are presented in order to verify the correct performance of the proposed control structure.
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