Statistical visual-dynamic model for hand-eye coordination

Daniel Beale; Pejman Iravani; Peter Hall

doi:10.1109/IROS.2010.5648832

Statistical visual-dynamic model for hand-eye coordination

Daniel Beale, Pejman Iravani, Peter Hall

Research output: Chapter or section in a book/report/conference proceeding › Book chapter

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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 language	English
Title of host publication	IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010 - Conference Proceedings
Place of Publication	Piscataway, NJ
Publisher	IEEE
Pages	3931-3936
Number of pages	6
ISBN (Electronic)	978-1-4244-6676-4
ISBN (Print)	9781424466757
DOIs	https://doi.org/10.1109/IROS.2010.5648832
Publication status	Published - Oct 2010
Event	23rd 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

Conference	23rd IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010, October 18, 2010 - October 22, 2010
City	Taipei, Taiwan
Period	1/10/10 → …

Bibliographical note

ID number: 5648832

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10.1109/IROS.2010.5648832

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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. IEEE, Piscataway, NJ, pp. 3931-3936, 23rd IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010, October 18, 2010 - October 22, 2010, Taipei, Taiwan, 1/10/10. https://doi.org/10.1109/IROS.2010.5648832

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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.",

author = "Daniel Beale and Pejman Iravani and Peter Hall",

note = "ID number: 5648832; 23rd IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010, October 18, 2010 - October 22, 2010 ; Conference date: 01-10-2010",

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doi = "10.1109/IROS.2010.5648832",

language = "English",

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N2 - 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.

AB - 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.

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DO - 10.1109/IROS.2010.5648832

M3 - Book chapter

SN - 9781424466757

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EP - 3936

BT - IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010 - Conference Proceedings

PB - IEEE

CY - Piscataway, NJ

T2 - 23rd IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010, October 18, 2010 - October 22, 2010

Y2 - 1 October 2010

ER -

Statistical visual-dynamic model for hand-eye coordination

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