Because of the COVID-19 outbreak, we now need to work on the project remotely. As a result, I spent my time this week working with my teammates to rescope the project. We have decided to keep the IMU I2C circuit component on the arm and to simulate the rest of the project using the Processing software.
I was focused on figuring out the details of the simulation and how the architecture of the system would change. The main challenge is modeling real-world behavior in the simulation. One example is modeling the ball’s actual path through the air to the actual landing location. To do this, the thrower will actually throw the ball in real-life and the actual landing location and time of travel will be measured. The data will be fed into the simulation.
I also thought about how to simulate the behavior of hardware we will no longer be using. One problem I had to solve is modeling the actual speed of a motor in the PID control system given that the robot will be simulated. Another issue is simulating the IMU on the robot which was going to be used to help keep track of the displacement left for the robot to travel to reach the predicted landing location. The specifics are included in the statement of work document, which we have worked on this week.
Additionally, I spent time learning more about how Kalman filters work and have gone through 25 out of the 55 Michel van Biezen Kalman Filter tutorial videos: https://www.youtube.com/watch?v=CaCcOwJPytQ&list=PLX2gX-ftPVXU3oUFNATxGXY90AULiqnWT&index=1
I learned how Kalman filters work in 1d, 2d and 3d and now have a good idea of how the measured values, estimated values, error in measured values, error in estimated values are used to predict ideal sensor values. I studied Kalman filter matrix operations and now understand how the Kalman Gain and the following matrices are used: state matrix, process noise covariance matrix, control variable matrix and measurement covariance matrix.
I will now go over the Kalman filter code Dan has been working with and help him finish the implementation of the IMU Kalman filters. We need to finish this part as soon as possible because it is one of the most important and difficult parts of the project.
0 Comments