Tag Archives: BNO055

9-Axis IMU LESSON 12: Passing Data From Arduino to Python

In this lesson we show how to pass data from Arduino to Python using a Com Port. This is important for our 9-Axis IMU project as we want to take advantage of the processing power and 3D graphics capabilities of Python. Our goal is to get the date from Arduino to Python, and then create a dynamic 3D visualization of our system. The first step in this goal is to pass the data from arduino to Python.

In order to do this, a first step is to install the pyserial library. If you followed our python installation tutorial in lesson 11, then it is easy to install pyserial by just opening a windows command prompt, and then typing:

pip install pyserial

If this does not work, likely you did not install python according to the instruction in lesson 11.

In order to show a simple demonstration of passing data, we can use the following code on the arduino side, which just generates x, y, and z numbers and passes them to Python.

We can grab these numbers from the Com port on the Python side with the following code. Note that you should use the com port your arduino is on, which likely will not be the same as mine (which was ‘com5’).

The above example is just a simple method for passing different channels from Arduino to Python.

 

For our IMU project, we want to use the code we left off with Lesson 10. However, note we can scale back on the number of data channels, because we just want the calibration data and then the final roll, pitch and yaw numbers. This is the arduino code that will pass those parameters.

Then, on the Python side we can grab and parse the data with this code.

In the next lesson we will install Vpython and begin building our code to create dynamic 3D visualizations of our system.

9-Axis IMU LESSON 10: Making a Tilt Compensated Compass with Arduino

In this lesson we show you how to build a demo tilt compensated compass using the BNO055 9-axis sensor.  We go through some trigonometry to help you understand conceptually how the device works.

To play along at home, you will need an Arduino Nano, and an Adafruit BNO055 Inertial Measurement Sensor.

The code below is provided for your convenience. It is intended only for bench top demos, and should not be used in real applications. Just for fun, not for drones, or other actual control applications.

 

9-Axis IMU LESSON 9: Accurate and Stable Tilt Using Accelerometers, Gyros and a Complimentary Filter

In this lesson we show how a complimentary filter can be used to create the best of both worlds in approximating tilt from data coming from the accelerometers and gyros. The goal of our complimentary filter is to weight our overall result to take advantage the short term accuracy of the gyro measurement, and combine it with the long term stability of the accelerometer. In effect the combination is applying a high pass filter to the gyro and a low pass filter to the accelerometer. Remember that the angles are approximate, and were developed assuming roll and pitch stay under 45 degrees. The trigonometry becomes more difficult when considering higher tilt angles.

To play along at home, you will need an Arduino Nano, and an Adafruit BNO055 Inertial Measurement Sensor.

The code below is for demo purposes only, and should not be used in any real applications. It just demonstrates how a complimentary filter works. Of course, any real systems need to go into much more depth than the simple demo project below.

 

9-Axis IMU LESSON 8: Using Gyros for Measuring Rotational Velocity and Angle

In this lesson we explore approximating the roll and pitch of our sensor using only the gyros. The advantage of gyros is that they are not susceptible to vibration as much as the acceleromters. In the video we show you how you can simply approximate roll and pitch from the data coming from the gyroscopes. Note that while the gyros do not have the noise problem seen in the accelerometers, we now have a new problem that the gyros are susceptible to long term drift. As you play with these devices what you end up seeing is you will need to combine the data from the accelerometers and the gyros in a clever way to take advantage of the long term stability of the accelerometers and the noise immunity of they gyros. In effect, you will want to apply a high pass filter to the gyro data, and a low pass filter to the acceleromters.

To follow along at home, you will need an Arduino Nano, and an Adafruit BNO055 Inertial Measurement Sensor. We suggest using identical hardware if you want to follow along as different sensors have very different characteristics, and things will work much better for you if we are using the same sensor

This is the code which we developed in the video to demonstrate these concepts.

The code below is for demo purposes only, and should not be used in any real applications. It just demonstrates how to work with this sensor in benchtop presentations.

 

9-Axis IMU LESSON 7: Understanding Low Pass Filters


In this project, we have shown how accelerometers can be used to approximate tilt. We showed in Lesson 6 how vibration can cause a problem with such a strategy, as the system will interpret vibration to be changes in tilt. A partial solution to this problem can be to program a low pass filter into the project. The video above introduces you to the concept of a low pass filter, and shows some simple examples of programming and using a low pass filter to reduce noise.

The code below is for demo purposes only, and should not be used in any real applications. It just demonstrates how to work with this sensor in benchtop presentations.