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State estimation for a hexapod robot

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dc.contributor.author Lubbe, Estelle
dc.contributor.author Withey, Daniel J
dc.contributor.author Uren, KR
dc.date.accessioned 2016-10-13T13:57:03Z
dc.date.available 2016-10-13T13:57:03Z
dc.date.issued 2015-09
dc.identifier.citation Lubbe, E., Withey, D.J., and Uren, K.R. 2015. State estimation for a hexapod robot. In: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Congress Center Hamburg, 28 September - 2 October 2015, Hamburg, Germany, 6pp en_US
dc.identifier.uri http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7354274
dc.identifier.uri http://hdl.handle.net/10204/8841
dc.description 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Congress Center Hamburg, 28 September - 2 October 2015, Hamburg, Germany. Due to copyright restrictions, the attached PDF file only contains the abstract of the full text item. For access to the full text item, please consult the publisher's website en_US
dc.description.abstract This paper introduces a state estimation methodology for a hexapod robot that makes use of proprioceptive sensors and a kinematic model of the robot. The methodology focuses on providing reliable full pose state estimation for a commercially-available hexapod robot platform with the use of only commonly-available sensors. The presented methodology provides the derivation of the kinematic model and implements an Extended Kalman Filter (EKF) state estimation framework similar to that recently validated on a quadruped. The EKF fuses the kinematic model with on-board IMU measurements to estimate the pose of the robot. The methodology was tested with experiments using a physical hexapod robot and validated with independent ground truth measurements. en_US
dc.language.iso en en_US
dc.publisher IEEE en_US
dc.relation.ispartofseries Worklist;15983
dc.subject Hexapod robots en_US
dc.subject Extended Kalman Filter en_US
dc.subject EKF en_US
dc.subject Kinematic models en_US
dc.title State estimation for a hexapod robot en_US
dc.type Conference Presentation en_US
dc.identifier.apacitation Lubbe, E., Withey, D. J., & Uren, K. (2015). State estimation for a hexapod robot. IEEE. http://hdl.handle.net/10204/8841 en_ZA
dc.identifier.chicagocitation Lubbe, Estelle, Daniel J Withey, and KR Uren. "State estimation for a hexapod robot." (2015): http://hdl.handle.net/10204/8841 en_ZA
dc.identifier.vancouvercitation Lubbe E, Withey DJ, Uren K, State estimation for a hexapod robot; IEEE; 2015. http://hdl.handle.net/10204/8841 . en_ZA
dc.identifier.ris TY - Conference Presentation AU - Lubbe, Estelle AU - Withey, Daniel J AU - Uren, KR AB - This paper introduces a state estimation methodology for a hexapod robot that makes use of proprioceptive sensors and a kinematic model of the robot. The methodology focuses on providing reliable full pose state estimation for a commercially-available hexapod robot platform with the use of only commonly-available sensors. The presented methodology provides the derivation of the kinematic model and implements an Extended Kalman Filter (EKF) state estimation framework similar to that recently validated on a quadruped. The EKF fuses the kinematic model with on-board IMU measurements to estimate the pose of the robot. The methodology was tested with experiments using a physical hexapod robot and validated with independent ground truth measurements. DA - 2015-09 DB - ResearchSpace DP - CSIR KW - Hexapod robots KW - Extended Kalman Filter KW - EKF KW - Kinematic models LK - https://researchspace.csir.co.za PY - 2015 T1 - State estimation for a hexapod robot TI - State estimation for a hexapod robot UR - http://hdl.handle.net/10204/8841 ER - en_ZA


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