Posted on

Mahlet’s Status Report for 11/16/2024

This week, I was able to successfully finalize the audio localization mechanism. 

Using matlab, I have been able to successfully pinpoint the source of an audio cue with an error margin of 5 degrees. This is also successful for our intended range of 0.9 meters,  or 3 feet. This is tested using generated audio signals in simulation. The next step for the audio localization is to integrate it with the microphone inputs. I take in an audio input signal and pass it in through a bandpass to isolate the audio cue we are responding to. The microphone then keeps track of the audio signals, in each microphone for the past 1.5 seconds, and uses the estimation mechanism to pinpoint the audio source. 

In addition to this, I have 3D printed the mount design that connects the servo motor to the head of the robot. This will allow for a seamless rotation of the robot head, based on the input detected. 

Another key accomplishment this week is the servo motor testing. I ran into some problems with our RPi’s compatibility with the recommended libraries. I have tested the servo on a few angles, and have been able to get some movement, but the calculations based on the PWM are slightly inaccurate.

The main steps for servo and audio neck accuracy verification is as follows. 

Verification 

The audio localization testing on simulation has been conducted by generating signals in matlab. The function was able to accurately identify the audio cue’s direction. The next testing will be conducted on the microphone inputs. This testing will go as follows: 

  1. In a quiet setting, clap twice within a 3 feet radius from the center of the robot. 
  2. Take in the clap audio and isolate ambient noise through the bandpass filter. Measure this on a waveform viewer to verify the accuracy of the bandpass filter. 
  3. Once the clap audio is isolated, make sure correct signals are being passed into each microphone using a waveform viewer. 
  4. Get the time it takes for this waveform to be correctly recorded, and save the signal to estimate direction.
  5. Use the estimate direction function to identify the angle of the input. 

To test the servo motors, varying angle values in the range of 0 and 180 will be applied. Due to the recent constraint of neck motion of the robot, if the audio cue’s angle is in the range of 180 and 270, the robot will turn to 180. If the angle is in the range of 270 and 360, the robot will turn to 0. 

  1. To verify the servo’s position accuracy, we will use an oscilloscope to verify the servo’s PWM, and ensure proportional change of position relative to time. 
  2. This will also be verified using visual indicators, to ensure reasonable accuracy. 

Once the servo position has been verified, the final step would be to connect the output of the estimate_direction to the servo’s input_angle function. 

My goal for next week is to:

  1. Accurately calculate the servo position
  2. Perform testing on the microphones per the verification methods mentioned above
  3. Translate the matlab code to python for the audio localization
  4. Begin final SBB body integrating

 

Posted on

Mahlet’s Status Report 11/09/2024

This week, I worked on Audio localization mechanism, servo initialization through the RPi and ways of mounting the servo to the robot head for seamless rotation of the head. 

Audio localization: 

I have a script that records audio for a specified duration, in our case would be every 1.5 seconds, and this will take in an input audio and filter out the clap sound from the surrounding using a bandpass filter. This audio input from each mic is then passed into the function that performs the direction estimation by performing cross correlation between each microphone. 

I have finalized the mathematical approach using the four microphones. After calculating the time difference of arrival between each microphone, I have been able to get close to the actual input arrival differences with slight variations. These are causing very unstable direction estimation to a margin of error to up to 30 degrees. The coming week, I will be working on cleaning up this error to ensure a smaller margin of error, and a more stable output. 

I also did some testing by using only three of the microphones in the orientation (0,0), (0, x), (y, 0) as an alternative approach. x and y are the dimensions of the robot(x = 8 cm, y = 7cm). This yields slightly more inaccurate results. I will be working on fine-tuning the 4 microphones, and as needed, I will modify the microphone positions to get the most optimal audio localization result.

Servo and the RPi: 

The Raspberry pi has a built-in library called python3-rpi.gpio, which initializes all the GPIO pins on the raspberry pi. The servo motor connects to the power, ground and a GPIO pin which receives the signal. The signal wire connects to a PWM GPIO pin, to allow for precise control over the signal that is sent to the servo. This pin can be plugged into GPIO12 or GPIO13. 

After this, I specify that the pin is an output and then initialize the pin. I use the set_servo_pulsewidth function to set the pulse width of the servo based on the angle from the audio localization output. 

Robot Neck to servo mounting solution: 

I designed a bar to mount the robot’s head to the servo motor while it’s housed in the robot’s body. 

The CAD for this design is as follows.

By next week, I plan to debug the audio triangulation and minimize the margin of error. I will also 3D print the mount and integrate it with the robot, and begin integration testing of these systems.