The high quality of heading orientation is a key factor to monitor the industrial robot arms in the Industry 4.0. The paper proposes a new algorithm ‘No Motion No Integration’ (NMNI) to enhance the gyroscope performance via signal processing and specifically improve the Madgwick filter for heading estimation to overcome the weakness of magnetometer and Global Navigation Satellite System (GNSS). The experiment was carried out to characterize the pros and cons of traditional Madgwick and new approached Madgwick. The results show that the proposed method displayed high effective measurements. The gyroscope drift was minimized to attain a good yaw angle without a magnetometer or GNSS.

Pre-Processing Technique for Compass-less Madgwick in Heading Estimation for Industry 4.0 / Hoang, MINH LONG; Pietrosanto, Antonio; Dello Iacono, Salvatore; Paciello, Vincenzo. - (2020), pp. -6. [10.1109/i2mtc43012.2020.9128969]

Pre-Processing Technique for Compass-less Madgwick in Heading Estimation for Industry 4.0

Minh Long Hoang
;
2020-01-01

Abstract

The high quality of heading orientation is a key factor to monitor the industrial robot arms in the Industry 4.0. The paper proposes a new algorithm ‘No Motion No Integration’ (NMNI) to enhance the gyroscope performance via signal processing and specifically improve the Madgwick filter for heading estimation to overcome the weakness of magnetometer and Global Navigation Satellite System (GNSS). The experiment was carried out to characterize the pros and cons of traditional Madgwick and new approached Madgwick. The results show that the proposed method displayed high effective measurements. The gyroscope drift was minimized to attain a good yaw angle without a magnetometer or GNSS.
2020
Pre-Processing Technique for Compass-less Madgwick in Heading Estimation for Industry 4.0 / Hoang, MINH LONG; Pietrosanto, Antonio; Dello Iacono, Salvatore; Paciello, Vincenzo. - (2020), pp. -6. [10.1109/i2mtc43012.2020.9128969]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11381/2968853
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