Localization (Real)

Lab Objective

In lab 10, I demonstrated the effectiveness of the Bayes filter in simulation. In this lab I will perform localization with the Bayes filter on the actual robot. Only the update step of the Bayes filter will be used as the motion is very noisy and therefore the prediction step will not be helpful.

Bayes Filter Simulation

Three files where provided to us to help us accomplishing this lab: lab11_sim.ipynb, lab11_real.ipynb and localization_extras.

• The lab11_sim.ipynb file is a Jupyter notebook that provides skeleton code to integrate the real robot with the localization code.
• The lab11_real.ipynb file is a Jupyter notebook that demonstrates the Bayes filter implementation on the virtual robot.
• localization_extras.py provides a fully-functional localization module that works on the virtual robot.

Our first task was to test localization in simulation again, but this time with the code given to us instead of our own implementation. I did this by running the simulation notebook and the result is shown below.

Bayes Filter (Real)

We were given an optimized Bayes filter, which was was used in Python for the localization process. The primary objective was to enable the robot to rotate precisely on its axis and gather ToF readings. This task is similiar to lab 9, where the focus was to map the environment. So, the PID controller was already optimized for rotating and gather readings and therefore, I reused that code. We had to implement this within the perform_observation_loop() function, which is a part of the RealRobot() class. When this function is called, the robot will rotate a full rotation, and output a numpy array of ToF distance values and angular position values. I decided to modify my notification handler from previous labs and use that to interpret the bluetooth values. Note that I also used the asyncio function so that the script will wait for the robot to finish its spin before proceeding. See my code below.

This collects 18 Time of Flight sensor readings, beginning with the robot at 0 degrees, and rotating in increments of 20 degrees to 340 degrees. In lab 9, I collected as many data points as possible but the world.yaml helper file expects 18 data points so this part was modified for this task. By rotating the robot exactly 20 degrees and stopping it to take a distance measurement before starting to rotate again, the data collection was very precise, which is important in order to localize the right spot.

Results

My results indicate that my robot has a very high confidence in its update position as the probability that the robot is at its belief is nearly 1 for all locations. The predicted locations from the update step also matches well with the actual ground truth of the robot. All of the localization results accurately determined the x coordinate. However, some of the locations were of by one foot in the y direction. The blue point represents the belief of the robot and the red point represents the ground truth. Also note that the robot think it is offset by around 10 degrees, which is accurate as my robot still rotates around 10 degrees too much. My intention is to fix this before lab 12 in order to succesfully navigate through the map.

Location: (-3, -2)

Location: (5, -3)

Location: (5, 3)

Location: (0, 3)

Location: (0, 0)