Proceedings of the ASME 2010 International Mechanical Engineering Congress & Exposition IMECE2010 November 12-18, 2010, Vancouver, British Columbia, Canada IMECE2010-40495 THE INTERNET-BASED TELEOPERATION: MOTION AND FORCE PREDICTIONS USING THE PARTICLE FILTER METHOD Jae-young Lee School of Engineering Science Simon Fraser University Burnaby, BC, Canada jla155@sfu.ca Shahram Payandeh School of Engineering Science Simon Fraser University Burnaby, BC, Canada shahram@cs.sfu.ca Ljiljana Trajković School of Engineering Science Simon Fraser University Burnaby, BC, Canada ljilja@cs.sfu.ca ABSTRACT In this paper, we present motion and force predictions in Internet-based teleoperation systems using the particle filter method. The particle filter, also known as the sequential Monte Carlo (SMC) method, is a probabilistic prediction or estimation technique within a sequential Bayesian framework: Data at a current time step are predicted or estimated by recursively generating probability distribution based on previous observations and input states. In this paper, we first formulate the particle filter method using a prediction-based approach. Motion and force data flows, which may be impaired by the Internet delay, are formulated within a sequential Bayesian framework. The true motion and force data are then predicted by employing the prediction-based particle filter method using the impaired observations and previous input states. We performed experiments using a haptic device that interacts with a mechanics-based virtual 3D graphical environment. The haptic device is used as a master controller that provides positioning inputs to a 4-degree of freedom (4-DoF) virtual robotic manipulator while receiving feedback force through interactions with the virtual environment. We simulate the Internet delay with variations typically observed in a user datagram protocol (UDP) transmission between the master controller and the virtual teleoperated robot. In this experimental scenario, the particle filter method is implemented for both motion and force data that experience the Internet delay. The proposed method is compared with the conventional Kalman filter. Experimental results indicate that in nonlinear and non-Gaussian environments the prediction-based particle filter has distinct advantage over other methods. INTRODUCTION An Internet-based teleoperation system is an interactive application where though a master device, a human operator transmits motion data while simultaneously receiving reflecting force data from a slave robot controller. Unlike other Internet applications that mainly focus on the reliable data transmission, interactive applications are highly delay-sensitive. The Internet delay, which is unknown and varies over time according to network conditions, may cause instability of an overall teleoperation system. Furthermore, the transmitted motion and force data are often impaired by significant delay and delay jitter during the Internet transmission [1]. Various approaches have been suggested in order to solve the time delay issue of Internet-based teleoperation systems. In the area of control systems, the wave variables transformation and its extensions have focused on the stability of overall teleoperation systems in the presence of constant delay [2], [3], 1 Copyright © 2010 by ASME