@proceedings {1703,
	title = {Interaction control for rehabilitation robotics via a low-cost force sensing handle},
	year = {2013},
	address = {Palo Alto, CA},
	attachments = {https://mahilab.rice.edu/sites/default/files/publications/Erwin2013\%20-\%20RiceWrist-Grip.pdf},
	author = {Andrew Erwin and Fabrizio Sergi and Vinay Chawda and Marcia K. O{\textquoteright}Malley}
}
@proceedings {1702,
	title = {A Method for Selecting Velocity Filter Cutoff Frequency for Maximizing Impedance Width Performance in Haptic Interfaces},
	year = {2013},
	month = {10/2013},
	address = {Palo Alto, CA},
	attachments = {https://mahilab.rice.edu/sites/default/files/publications/Velocity\%20filtering_DSCC2013_final_version.pdf},
	author = {Vinay Chawda and Ozkan Celik and Marcia K. O{\textquoteright}Malley}
}
@proceedings {1473,
	title = {On the Performance of Passivity-based Control of Haptic Displays Employing Levant{\textquoteright}s Differentiator for Velocity Estimation},
	year = {2012},
	month = {03/2012},
	pages = {415-419},
	publisher = {IEEE},
	address = {Vancouver, BC, Canada},
	abstract = {<p>In impedance-type haptic interfaces, encoders are typically employed to provide high resolution position measurements from which velocity is estimated, most commonly via the finite difference method (FDM). This velocity estimation technique performs reliably, unless very fast sampling is required, in which case noise or delay due to filtering of the position signals reduces accuracy in the estimate. Despite this limitation, FDM is attractive because it is a passive process, and therefore the passivity of the overall system can be guaranteed. Levant{\textquoteright}s differentiator is a viable alternative to FDM, and exhibits increased accuracy in velocity estimation at high sample rates compared to FDM. However, the passivity of this nonlinear velocity estimation technique cannot be shown using conventional methods. In this paper, we employ a time domain passivity framework to analyze and enforce passive behavior of Levant{\textquoteright}s differentiator for haptic displays in discrete time. The performance of this approach is explored both in simulation and experimentally on a custom made one degree-of-freedom haptic interface. Results demonstrate the effectiveness of the time domain passivity approach for compensating the active behavior observed with use of Levant{\textquoteright}s differentiator for velocity estimation.</p>},
	isbn = {978-1-4673-0808-3},
	doi = {10.1109/HAPTIC.2012.6183824},
	attachments = {https://mahilab.rice.edu/sites/default/files/publications/chawda.pdf},
	author = {Vinay Chawda and Marcia K. O{\textquoteright}Malley}
}
@proceedings {1114,
	title = {Application of Levant{\textquoteright}s Differentiator for Velocity Estimation and Increased Z-Width in Haptic Interfaces},
	year = {2011},
	month = {06/2011},
	pages = {403-408},
	publisher = {IEEE},
	address = {Istanbul, Turkey},
	issn = {978-1-4577-0297-6},
	attachments = {https://mahilab.rice.edu/sites/default/files/publications/1114-chawda.pdf},
	author = {Vinay Chawda and Ozkan Celik and Marcia K. O{\textquoteright}Malley}
}
@proceedings {chawda2011,
	title = {A Lyapunov Approach for SOSM Based Velocity Estimation and its Application to Improve Bilateral Teleoperation Performance},
	year = {2011},
	attachments = {https://mahilab.rice.edu/sites/default/files/publications/1322-DSCC2011-6181.pdf},
	author = {Vinay Chawda and Marcia K. O{\textquoteright}Malley}
}
@article {1079,
	title = {Vision-Based Force Sensing for Nanomanipulation},
	journal = {IEEE /ASME Transactions on Mechatronics},
	year = {2011},
	type = {Journal Article},
	abstract = {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. The technique was tested on a scaled model of the nanoscale AFM cantilever and verified using theoretical estimates as well as direct strain measurements. Artificial SEM noise was introduced in the macroscale images to characterize our sensor under varying levels of noise and other SEM effects. Prior knowledge about the cantilever is not required, and the algorithm runs independent of human input. The method is shown to be effective under varying noise levels, and demonstrates improving performance as magnification levels are decreased. Therefore, we conclude that the vision-based force sensing algorithm is best suited for continuous operation of the SEM, fast scanning rates, and large fields-of-view associated with low magnification levels.},
	issn = {1083-4435 },
	doi = {10.1109/TMECH.2010.2093535},
	url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5692832},
	attachments = {https://mahilab.rice.edu/sites/default/files/publications/1079-05692832.pdf},
	author = {Vinay Chawda and O{\textquoteright}Malley, M.K.}
}
