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Razširjeni Kalmanov filter za določanje lege mobilnega robota na osnovi inercialne merilne enote in kodirnikov
ID Zavodnik, Vid (Author), ID Vrabič, Rok (Mentor) More about this mentor... This link opens in a new window

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Abstract
Sodobni mobilni roboti pogosto vsebujejo več senzorjev za merjenje povezanih fizikalnih veličin. Uporaba razširjenega Kalmanovega filtra omogoča združevanje podatkov tako, da je rezultat zanesljivejši od vsake posamezne meritve s senzorja. Na mobilnem robotu z diferencialnim pogonom, opremljenim z inercialno merilno enoto in kolesnimi kodirniki, smo prikazali delovanje EKF, z namenom izboljševanja zanesljivosti lokalizacije robota. Robot temelji na strojno-programski opremi ROS (\textit{angl. Robotic operating system}), kar nam je omogočilo uporabo odprtokodnega programskega paketa robot\_localization, v katerem je ta filter implementiran. Preizkus sledenja z EKF smo izvedli z vožnjo robota po progi in rezultate primerjali z lego, kot jo prikazuje zunanja kamera. Ugotovili smo, da so podatki iz inercialne merilne enote preveč nezanesljivi, da bi njihovo vključevanje izboljšalo lokalizacijo robota. Kljub temu smo zaključili, da tudi samo filtriranje podatkov iz kodirnikov poveča zanesljivost lokalizacije, saj nadaljuje z napovedjo stanja robota takrat, ko so podatki s senzorjev začasno nedostopni.

Language:Slovenian
Keywords:mobilni roboti, diferencialni pogoni, senzorji, lokalizacija, razširjeni Kalmanov filter, strojno-programska oprema ROS
Work type:Bachelor thesis/paper
Typology:2.11 - Undergraduate Thesis
Organization:FS - Faculty of Mechanical Engineering
Place of publishing:Ljubljana
Publisher:[V. Zavodnik]
Year:2019
Number of pages:XX, 50 str.
PID:20.500.12556/RUL-109622 This link opens in a new window
UDC:007.52:681.5:004.4(043.2)
COBISS.SI-ID:16911131 This link opens in a new window
Publication date in RUL:06.09.2019
Views:1507
Downloads:266
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Secondary language

Language:English
Title:Extended Kalman filter for determining the pose of a mobile robot based on inertial measurement unit and encoders
Abstract:
Modern mobile robots often include multiple sensors that measure correlated physical quantities. The use of an extended Kalman filter allows data to be fused in a way that the result is more reliable than any individual sensor measurement. On a differential driven mobile robot equipped with an inertial measuring unit and wheel encoders, we demonstrated the use of EKF in order to improve reliability of the robot's tracking. Tested robot is based on ROS firmware, which allowed us to use the open source robot\_localization software package in which this filter is already implemented. The EKF tracking test was performed by driving the robot along a track and comparing the results to the position acquired from the external camera. We have found that data from the inertial measurement unit is not reliable enough to improve on the robots localization. Nevertheless, we concluded that filtering data from encoders alone increases the reliability of localization, as the filter continues to predict the status of the robot, even when sensor data is temporarily unaccessible.

Keywords:mobile robots, differential drives, sensors, localization, extended Kalman filter, ROS firmware

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