Filtering Strategies for State Estimation of Omniwheel Robots

dc.contributor.author	Dyer BM
dc.contributor.author	Smith TR
dc.contributor.author	Gadsden SA
dc.contributor.author	Biglarbegian M
dc.contributor.department	Mechanical Engineering
dc.date.accessioned	2025-03-01T22:55:19Z
dc.date.available	2025-03-01T22:55:19Z
dc.date.issued	2020-10-16
dc.date.updated	2025-03-01T22:55:15Z
dc.description.abstract	Various state estimation strategies are investigated using a kinematic model of a four-wheel holonomic robot with Swedish wheels. A multi-tiered filtering strategy is implemented using Kalman Filters (KF) developed to estimate wheel velocity with an Extended Kalman Filter (EKF) and a Smooth Variable Structure Filter (SVSF) developed for state estimation of the robot. The use of only KFs on the wheels, only EKF or SVSF on the robot, and KFs on the wheels with either an EKF or SVSF on the robot is tested. Simulation results show that using a KF on each wheel in conjunction with either a SVSF or EKF on the robot yields an order of magnitude better state estimation compared to other configurations allowing for increased control of the robot.
dc.identifier.doi	https://doi.org/10.1109/icma49215.2020.9233826
dc.identifier.uri	http://hdl.handle.net/11375/31284
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.subject	4007 Control Engineering, Mechatronics and Robotics
dc.subject	40 Engineering
dc.subject	4010 Engineering Practice and Education
dc.title	Filtering Strategies for State Estimation of Omniwheel Robots
dc.type	Article

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