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 . 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.
The objective of this research effort is to develop a rehabilitation robot and associated controllers to be used in both therapy and evaluation of subjects with incomplete spinal-cord injuries. We are working in collaboration with Dr. Gerard Francisco and Dr. Nuray Yozbatiran of TIRR-Memorial Hermann and UTHealth.
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.
A vision-based algorithm for estimating tip interaction forces on a deflected Atomic Force Microscope (AFM) cantilever is described. Specifically, we propose that the algorithm can estimate forces acting on an Atomic Force Microscope (AFM) cantilever being used as a nanomanipulator inside a Scanning Electron Microscope (SEM). The vision based force sensor can provide force feedback in real-time, a feature absent in many SEMs. A methodology based on cantilever slope detection is used to estimate the forces acting on the cantilever tip.
As yet underdeveloped is the psychology of human learning as it pertains to manual control tasks in fully dynamic, multi-degree-of-freedom domains. While we currently possess the capacity to teach these tasks, we are unable to predict how well people will do in these domains or how rapidly they will learn.