Statistical visual-dynamic model for hand-eye coordination

Daniel Beale, Pejman Iravani, Peter Hall

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

This paper introduces a new statistical method for combining vision and robot dynamics to generate trajectories to intercept a moving object. Previous methods only use information from the kinematics without considering the forces needed to move along the trajectory. Using robot dynamics allows extra measures, such as energy efficiency, to be optimised alongside maximising the likelihood of intercepting the target. We derive a statistical model for a vision system and a Lagrangian dynamical model of a robotic arm, showing how to relate joint torques to the vision. The method is tested by applying it to the problem of catching a simulated moving object.
Original languageEnglish
Title of host publicationIEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010 - Conference Proceedings
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages3931-3936
Number of pages6
ISBN (Electronic)978-1-4244-6676-4
ISBN (Print)9781424466757
DOIs
Publication statusPublished - Oct 2010
Event23rd IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010, October 18, 2010 - October 22, 2010 - Taipei, Taiwan
Duration: 1 Oct 2010 → …

Conference

Conference23rd IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010, October 18, 2010 - October 22, 2010
CityTaipei, Taiwan
Period1/10/10 → …

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Beale, D., Iravani, P., & Hall, P. (2010). Statistical visual-dynamic model for hand-eye coordination. In IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010 - Conference Proceedings (pp. 3931-3936). Piscataway, NJ: IEEE. https://doi.org/10.1109/IROS.2010.5648832