Although soft robotic assistive gloves have high potential for restoring functional independence for individuals with motor impairment, their lack of rigid components makes it difficult to obtain accurate position sensing to validate their performance. To track soft device motion, standard practices rely on costly optical motion capture techniques, which have reduced accuracy due to limitations in marker occlusion and device deformation.

Despite quantifiable gains in motor function as a result of intensive upper limb rehabilitation using robotic devices, individuals with incomplete spinal cord injury (SCI) still desire increased independence that requires improved hand function. We have designed a soft robotic glove that can assist in activities of daily living (ADL). The soft, flexible glove has cable-like tendons that run along the fingers on both the palm and back of hand.

Rigid haptic devices enable humans to physically interact with virtual environments, and the range of impedances that can be safely rendered using these rigid devices is quantified by the Z-Width metric. Series elastic actuators (SEAs) similarly modulate the impedance felt by the human operator when interacting with a robotic device, and, in particular, the robot's perceived stiffness can be controlled by changing the elastic element's equilibrium position.

Robots are increasingly designed to physically interact with humans in unstructured environments, and as such must operate both accurately and safely. Leveraging compliant actuation, typically in the form of series elastic actuators (SEAs), can guarantee this required level of safety. To date, a number of frequency domain techniques have been proposed which yield effective SEA torque and impedance control; however, these methods are accompanied by undesirable stability constraints.

Through the use of functional magnetic resonance imaging (fMRI) in conjunction with a haptic device, it is possible to study changes in brain activity while a patient undergoes rehabilitation-like protocols. By measuring changes in brain activity of a patient undergoing neurorehabilitation during fMRI, optimal patient-specific therapy regimens might be obtained. This research aims to develop, characterize, and control a parallel three degrees of freedom magnetic resonance (MR) compatible haptic device, called the MR-SoftWrist, which can measure and support wrist movements during fMRI.

Interfaces and strategies for the teleoperation of bipedal humanoid robots, which otherwise hold great potential in industrial, space exploration, and military application, are currently under-researched.

The primary goal of the Haptic Paddle is to improve learning outcomes in a required undergraduate mechanical engineering course via reflective learning featuring integrated systems [1]. The labs integrate haptic technology, LabVIEW, MATLAB simulations, and system interfacing in experiments to enhance understanding of dynamic systems and controls. The specific objectives of the Haptic Paddle-centric lab curriculum are:

In many mechatronic applications, velocity estimation is required for implementation of closed loop control. Proportional-Integral control based differentiation has been proposed to estimate velocity in bilateral teleoperation. We propose a Second Order Sliding Mode (SOSM) based velocity estimation scheme for this application, since the SOSM approach is robust to small disturbances near the origin. Simulation results demonstrate the superior performance of the SOSM based velocity estimation over the PI-control approach for bilateral teleoperation in viscous environments.

In this study, we demonstrate application of Levant's differentiator for velocity estimation from optical encoder readings. Levant's differentiator is a sliding mode control theory-based real-time differentiation algorithm. The application of the technique allows stable implementation of higher stiffness virtual walls as compared to using the common finite difference method (FDM) cascaded with low-pass filters for velocity estimation.

Sensing of displacement using only inertial measurement devices (IMDs) such as rate gyros and accelerometers is an active research topic with many diverse applications in biomechanics, human motion, earthquake engineering, robotics and mixed reality interfaces.