Help with calculating Pitch and Roll from MPU6050

[davidleswell (Les well)]

Hi. I need some help calculating Pitch and Roll from MPU6050 data.
my code

esphome:
  name: solar_tracker
  platform: ESP32
  board: esp-wrover-kit


wifi:
  ssid: "."
  password: "."


  # Enable fallback hotspot (captive portal) in case wifi connection fails
  ap:
    ssid: 
    password:

captive_portal:

# Enable logging
logger:

# Enable Home Assistant API
api:

ota:

i2c:
  sda: 21
  scl: 22
  scan: True

sensor:
  - platform: mpu6050
    address: 0x68
    update_interval: 100ms
    
    accel_x:
      name: "MPU6050 Accel X"
      filters:
       - sliding_window_moving_average:
          window_size: 10
          send_every: 5 
       
          
    accel_y:
      name: "MPU6050 Accel Y"
      filters:
       - sliding_window_moving_average:
          window_size: 10
          send_every: 5 
    accel_z:
      name: "MPU6050 Accel z"
      filters:
       - sliding_window_moving_average:
          window_size: 10
          send_every: 5 
    gyro_x:
      name: "MPU6050 Gyro X"
      filters:
       - sliding_window_moving_average:
          window_size: 10
          send_every: 5 
    gyro_y:
      name: "MPU6050 Gyro Y"
      filters:
       - sliding_window_moving_average:
          window_size: 10
          send_every: 5 
    gyro_z:
      name: "MPU6050 Gyro z"
      filters:
       - sliding_window_moving_average:
          window_size: 10
          send_every: 5 
    temperature:
      name: "MPU6050 Temperature"
      filters:
       - sliding_window_moving_average:
          window_size: 10
          send_every: 5 
          

#relay bank Manual Switching
switch:
  - platform: gpio
    pin: 23
    id: relayUp
    name: "Solar Up"
  

  - platform: gpio
    pin: 19
    id: relayDown
    name: "Solar Down"
    
    
  - platform: gpio
    pin: 5
    id: relayEast
    name: "Solar East"
   

  - platform: gpio
    pin: 17
    id: relayWest
    name: "Solar West"

formula

// Calculating Roll and Pitch from the accelerometer data
  Roll = (atan(AccY / sqrt(pow(AccX, 2) + pow(AccZ, 2))) * 180 / PI) ;

  Pitch = (atan(-1 * AccX / sqrt(pow(AccY, 2) + pow(AccZ, 2))) * 180 / PI); 

I am unsure how to reference the raw data into the above formula.
any help would be very much appreciated.

[tfmeier (Thomas Meier)]

@davidleswell Have you ever found the answer? I’m exploring using the MPU-6050 to monitor changes in concrete footings

[FredTheFrog (Keith)]

Refer to @SpikeyGG post here for examples:

Laundry Sensors with NodeMCU and Home Assistant

Even thought I really like seeing all the raw stats for the washer and dryer solution, I didn’t like how all the decision making was pushed to Home Assistant. I always felt like this was a cop-out when I should have written the ESPHome solution to determine the state of the washer and dryer… so I did! The benefit of this is that it’s independent of the position of the MPU6050. Previously, I had painstakingly calibrated all my dimensions (accel_x, accel_y, accel_z, gyro_x, gyro_y, gyro_z for bot…

This is simply a motion/vibration sensor for washer and dryer. But the example is valuable because it shows how to define a global variable and then set the value of that global variable using a lambda. It also shows how to use the sensor values from the MPU6050 in your own calculation.

[omartinezgomez (OMartinez)]

sensor:
  - platform: mpu6050
    address: 0x68
    accel_x:
      id: accel_x
      name: "MPU6050 Accel X"
      filters:
       - sliding_window_moving_average:
          window_size: 10
          send_every: 5
    accel_y:
      id: accel_y
      name: "MPU6050 Accel Y"
      filters:
       - sliding_window_moving_average:
          window_size: 10
          send_every: 5
    accel_z:
      id: accel_z
      name: "MPU6050 Accel z"
      filters:
       - sliding_window_moving_average:
          window_size: 10
          send_every: 5
    gyro_x:
      name: "MPU6050 Gyro X"
      id: gyro_x
      filters:
       - sliding_window_moving_average:
          window_size: 10
          send_every: 5
    gyro_y:
      name: "MPU6050 Gyro Y"
      id: gyro_y
      filters:
       - sliding_window_moving_average:
          window_size: 10
          send_every: 5
    gyro_z:
      name: "MPU6050 Gyro z"
      id: gyro_z
      filters:
       - sliding_window_moving_average:
          window_size: 10
          send_every: 5
    temperature:
      name: "MPU6050 Temperature"
      filters:
       - sliding_window_moving_average:
          window_size: 10
          send_every: 5
    update_interval: 500ms

  - platform: template
    id: roll
    name: roll
    accuracy_decimals: 2
    lambda: |-
      return  (atan( id(accel_y).state / sqrt( pow( id(accel_x).state , 2) + pow( id(accel_z).state , 2) ) ) * 180 / PI) ;
    update_interval: 500ms

  - platform: template
    id: pitch
    name: pitch
    accuracy_decimals: 2
    lambda: |-
      return  (atan(-1 * id(accel_x).state / sqrt(pow(id(accel_y).state, 2) + pow(id(accel_z).state, 2))) * 180 / PI);
    update_interval: 500ms

[FredTheFrog (Keith)]

Hint: If you place a single line of three back-ticks/back-quotes above and below your code, it will present properly for others to review.

[omartinezgomez (OMartinez)]

Thanks. It is already corrected

[Rural_assist]

Just what I needed.
Thank You!

[felmatrix]

@omartinezgomez

  - platform: template
    id: roll
    name: roll
    accuracy_decimals: 2
    lambda: |-
      return  (atan( id(accel_y).state / sqrt( pow( id(accel_x).state , 2) + pow( id(accel_z).state , 2) ) ) * 180 / PI) ;
    update_interval: 500ms

Hi, with this code I have a problem! At 0 ° and 180 ° it is quite precise but at 90 ° I give me 75 ° and to 45 I give me about 42. How can I solve? Thank you