Full Content is available to subscribers

Subscribe/Learn More  >

The Internet-Based Teleoperation: Motion and Force Predictions Using the Particle Filter Method

[+] Author Affiliations
Jae-young Lee, Shahram Payandeh, Ljiljana Trajković

Simon Fraser University, Burnaby, BC, Canada

Paper No. IMECE2010-40495, pp. 765-771; 7 pages
  • ASME 2010 International Mechanical Engineering Congress and Exposition
  • Volume 8: Dynamic Systems and Control, Parts A and B
  • Vancouver, British Columbia, Canada, November 12–18, 2010
  • Conference Sponsors: ASME
  • ISBN: 978-0-7918-4445-8
  • Copyright © 2010 by ASME


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.

Copyright © 2010 by ASME



Interactive Graphics


Country-Specific Mortality and Growth Failure in Infancy and Yound Children and Association With Material Stature

Use interactive graphics and maps to view and sort country-specific infant and early dhildhood mortality and growth failure data and their association with maternal

Citing articles are presented as examples only. In non-demo SCM6 implementation, integration with CrossRef’s "Cited By" API will populate this tab (http://www.crossref.org/citedby.html).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In