Lubbe, EstelleWithey, Daniel JUren, KR2016-10-132016-10-132015-09Lubbe, 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, 6pphttp://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7354274http://hdl.handle.net/10204/88412015 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 websiteThis 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.enHexapod robotsExtended Kalman FilterEKFKinematic modelsConference PresentationLubbe, E., Withey, D. J., & Uren, K. (2015). State estimation for a hexapod robot. IEEE. http://hdl.handle.net/10204/8841Lubbe, Estelle, Daniel J Withey, and KR Uren. "State estimation for a hexapod robot." (2015): http://hdl.handle.net/10204/8841Lubbe E, Withey DJ, Uren K, State estimation for a hexapod robot; IEEE; 2015. http://hdl.handle.net/10204/8841 .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 -