Feedback State Estimation for Multi-rotor Drones Stabilisation Using Low-Pass Filter and a Complementary Kalman Filter

Kangunde, Vemema; Mohutsiwa, Lucky Odirile; Jamisola, Rodrigo S.

doi:10.1109/icuas51884.2021.9476809

2021 International Conference on Unmanned Aircraft Systems (ICUAS) 2021

DOI: 10.1109/icuas51884.2021.9476809

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Feedback State Estimation for Multi-rotor Drones Stabilisation Using Low-Pass Filter and a Complementary Kalman Filter

Vemema Kangunde

Lucky Odirile Mohutsiwa

Rodrigo S. Jamisola

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2024

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Affordable 3D Orientation Visualization Solution for Working Class Remotely Operated Vehicles (ROV)

Kasno,

Yahaya,

Jung

2024

Sensors

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ROV operators often encounter challenges with orientation awareness while operating underwater, primarily due to relying solely on 2D camera feeds to manually control the ROV robot arm. This limitation in underwater visibility and orientation awareness, as observed among Malaysian ROV operators, can compromise the accuracy of arm placement, and pose a risk of tool damage if not handle with care. To address this, a 3D orientation monitoring system for ROVs has been developed, leveraging measurement sensors with nine degrees of freedom (DOF). These sensors capture crucial parameters such as roll, pitch, yaw, and heading, providing real-time data on the ROV’s position along the X, Y, and Z axes to ensure precise orientation. These data are then utilized to generate and process 3D imaging and develop a corresponding 3D model of the operational ROV underwater, accurately reflecting its orientation in a visual representation by using an open-source platform. Due to constraints set by an agreement with the working class ROV operators, only short-term tests (up to 1 min) could be performed at the dockyard. A video demonstration of a working class ROV replica moving and reflecting in a 3D simulation in real-time was also presented. Despite these limitations, our findings demonstrate the feasibility and potential of a cost-effective 3D orientation visualization system for working class ROVs. With mean absolute error (MAE) error less than 2%, the results align with the performance expectations of the actual working ROV.

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Affordable 3D Orientation Visualization Solution for Working Class Remotely Operated Vehicles (ROV)

Kasno,

Yahaya,

Jung

2024

Sensors

View full text Add to dashboard Cite

show abstract

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Feedback State Estimation for Multi-rotor Drones Stabilisation Using Low-Pass Filter and a Complementary Kalman Filter

Cited by 1 publication

References 20 publications

Affordable 3D Orientation Visualization Solution for Working Class Remotely Operated Vehicles (ROV)

Affordable 3D Orientation Visualization Solution for Working Class Remotely Operated Vehicles (ROV)

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