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.

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.

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.

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.