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M-Estimator Application in Real-Time Sensor Fusion for Smooth Position Feedback of Heavy-Duty Field Robots

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Standard

M-Estimator Application in Real-Time Sensor Fusion for Smooth Position Feedback of Heavy-Duty Field Robots. / Liikanen, Henri; Aref, Mohammad M.; Mattila, Jouni.

Proceedings of the IEEE 2019 9th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). IEEE, 2019. s. 368-373 (IEEE International Conference on Cybernetics and Intelligent Systems).

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Harvard

Liikanen, H, Aref, MM & Mattila, J 2019, M-Estimator Application in Real-Time Sensor Fusion for Smooth Position Feedback of Heavy-Duty Field Robots. julkaisussa Proceedings of the IEEE 2019 9th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). IEEE International Conference on Cybernetics and Intelligent Systems, IEEE, Sivut 368-373, 1/01/00. https://doi.org/10.1109/CIS-RAM47153.2019.9095821

APA

Liikanen, H., Aref, M. M., & Mattila, J. (2019). M-Estimator Application in Real-Time Sensor Fusion for Smooth Position Feedback of Heavy-Duty Field Robots. teoksessa Proceedings of the IEEE 2019 9th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM) (Sivut 368-373). (IEEE International Conference on Cybernetics and Intelligent Systems). IEEE. https://doi.org/10.1109/CIS-RAM47153.2019.9095821

Vancouver

Liikanen H, Aref MM, Mattila J. M-Estimator Application in Real-Time Sensor Fusion for Smooth Position Feedback of Heavy-Duty Field Robots. julkaisussa Proceedings of the IEEE 2019 9th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). IEEE. 2019. s. 368-373. (IEEE International Conference on Cybernetics and Intelligent Systems). https://doi.org/10.1109/CIS-RAM47153.2019.9095821

Author

Liikanen, Henri ; Aref, Mohammad M. ; Mattila, Jouni. / M-Estimator Application in Real-Time Sensor Fusion for Smooth Position Feedback of Heavy-Duty Field Robots. Proceedings of the IEEE 2019 9th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). IEEE, 2019. Sivut 368-373 (IEEE International Conference on Cybernetics and Intelligent Systems).

Bibtex - Lataa

@inproceedings{3e148e707c564fe1ba1e1164f257e4a2,
title = "M-Estimator Application in Real-Time Sensor Fusion for Smooth Position Feedback of Heavy-Duty Field Robots",
abstract = "In this paper, we study the performance of a complementary filter with adaptive weights in a sensor fusion application for real-time localization of an omnidirectional field robot. The test-case robot is a large, four-wheel drive and steer (4WDS) construction vehicle with nonlinear internal dynamics and hydraulic driving and steering actuators. Our objective is to provide the vehicle's real-time controller with robust, smooth feedback that prevents unnecessary oscillations in steering, which can waste significant amounts of energy. We do so by assigning weights for measurements based on their consistency with the robot's motions. The calculations are based on two main data sources: (1) measured velocity vectors from wheel driving (odometer) and steering of the 4WDS test-case robot; and (2) data obtained from a differential global navigation satellite system on the absolute pose of the robot. We show that the sensor fusion is robust to the noise and single point failures of the sensors, while the maximum heading oscillations are reduced by 70{\%}-95{\%}, preserving the accuracy of the global positioning system. Moreover, we demonstrate the feasibility and efficacy of the real-time implementation of this filtering method in path-following control of the robot.",
keywords = "GNSS, GPS, heavy-dutyfield robot, motion estimation, path following, sensor fusion, wheel odometry",
author = "Henri Liikanen and Aref, {Mohammad M.} and Jouni Mattila",
year = "2019",
doi = "10.1109/CIS-RAM47153.2019.9095821",
language = "English",
isbn = "978-1-7281-3459-8",
series = "IEEE International Conference on Cybernetics and Intelligent Systems",
publisher = "IEEE",
pages = "368--373",
booktitle = "Proceedings of the IEEE 2019 9th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM)",

}

RIS (suitable for import to EndNote) - Lataa

TY - GEN

T1 - M-Estimator Application in Real-Time Sensor Fusion for Smooth Position Feedback of Heavy-Duty Field Robots

AU - Liikanen, Henri

AU - Aref, Mohammad M.

AU - Mattila, Jouni

PY - 2019

Y1 - 2019

N2 - In this paper, we study the performance of a complementary filter with adaptive weights in a sensor fusion application for real-time localization of an omnidirectional field robot. The test-case robot is a large, four-wheel drive and steer (4WDS) construction vehicle with nonlinear internal dynamics and hydraulic driving and steering actuators. Our objective is to provide the vehicle's real-time controller with robust, smooth feedback that prevents unnecessary oscillations in steering, which can waste significant amounts of energy. We do so by assigning weights for measurements based on their consistency with the robot's motions. The calculations are based on two main data sources: (1) measured velocity vectors from wheel driving (odometer) and steering of the 4WDS test-case robot; and (2) data obtained from a differential global navigation satellite system on the absolute pose of the robot. We show that the sensor fusion is robust to the noise and single point failures of the sensors, while the maximum heading oscillations are reduced by 70%-95%, preserving the accuracy of the global positioning system. Moreover, we demonstrate the feasibility and efficacy of the real-time implementation of this filtering method in path-following control of the robot.

AB - In this paper, we study the performance of a complementary filter with adaptive weights in a sensor fusion application for real-time localization of an omnidirectional field robot. The test-case robot is a large, four-wheel drive and steer (4WDS) construction vehicle with nonlinear internal dynamics and hydraulic driving and steering actuators. Our objective is to provide the vehicle's real-time controller with robust, smooth feedback that prevents unnecessary oscillations in steering, which can waste significant amounts of energy. We do so by assigning weights for measurements based on their consistency with the robot's motions. The calculations are based on two main data sources: (1) measured velocity vectors from wheel driving (odometer) and steering of the 4WDS test-case robot; and (2) data obtained from a differential global navigation satellite system on the absolute pose of the robot. We show that the sensor fusion is robust to the noise and single point failures of the sensors, while the maximum heading oscillations are reduced by 70%-95%, preserving the accuracy of the global positioning system. Moreover, we demonstrate the feasibility and efficacy of the real-time implementation of this filtering method in path-following control of the robot.

KW - GNSS

KW - GPS

KW - heavy-dutyfield robot

KW - motion estimation

KW - path following

KW - sensor fusion

KW - wheel odometry

U2 - 10.1109/CIS-RAM47153.2019.9095821

DO - 10.1109/CIS-RAM47153.2019.9095821

M3 - Conference contribution

SN - 978-1-7281-3459-8

T3 - IEEE International Conference on Cybernetics and Intelligent Systems

SP - 368

EP - 373

BT - Proceedings of the IEEE 2019 9th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM)

PB - IEEE

ER -