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The Training: Mastering the MPU6050 Gyroscope: From Hands-On to Advanced Implementation training is delivered in-person or remotely (blended-learning, e-learning, virtual classroom, remote in-person). At Learni, a Qualiopi-certified training organization, each program is designed to maximize skills acquisition, regardless of the training mode chosen.
The trainer alternates between demonstrative, interrogative, and active methods (through practical exercises and/or real-world scenarios). This pedagogical approach ensures concrete and directly applicable learning in the workplace.
To ensure the quality of the Training: Mastering the MPU6050 Gyroscope: From Hands-On to Advanced Implementation training, Learni provides the following teaching resources:
For in-house training at a location external to Learni, the client ensures and commits to having all necessary teaching materials (IT equipment, internet connection...) for the proper conduct of the training action in accordance with the prerequisites indicated in the communicated training program.
The assessment of skills acquired during the Training: Mastering the MPU6050 Gyroscope: From Hands-On to Advanced Implementation training is carried out through:
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Learni training programs are available for inter-company and intra-company settings, both in-person and remote. Registration is possible up to 48 business hours before the start of training. Our programs are eligible for OPCO, Pôle emploi, and FNE-Formation funding. Contact us to discuss your training project and funding possibilities.
Presentation of the MPU6050 sensor: architecture, physical principles, fields of application. Understanding the concept of a 6-axis inertial measurement unit (IMU). Introduction to associated communication protocols. First acquisition on Arduino: installation, wiring, libraries.
Deciphering the internal registers of the MPU6050. Reading raw signals via I2C. Handling interrupts, polling, and error management. Calibration of the accelerometer and gyroscope: bias compensation, filtering (Kalman, complementary), and conversion to physical units.
Data fusion to obtain orientation, inclination angle calculation. Implementation of fusion filters (complementary, Kalman). Usage on different microcontrollers (Raspberry Pi, ESP32, STM32). Implementation: robot stabilization, fall detection, gesture remote control. Advice on reliability, optimization, and final mini-project.
Target audience
Engineers, technicians, students, or makers wishing to integrate the MPU6050 sensor into their electronic or robotic projects
Prerequisites
Basic knowledge of electronics and Arduino programming (or similar microcontroller)
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