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Minor changes and cleanups

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This commit is contained in:
Oleg Kalachev 2023-12-19 05:25:57 +03:00
parent 77dd02dbeb
commit fde9c1cf36
4 changed files with 9 additions and 14 deletions
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3
flix/cli.ino
View File

@ -30,8 +30,7 @@ const char* motd =
"cg - calibrate gyro\n"
"ca - calibrate accel\n"
"fullmot <n> - test motor on all signals\n"
"reset - reset state\n"
"wifi - start wi-fi access point\n";
"reset - reset drone's state\n";
const struct Param {
const char* name;

1
flix/control.ino
View File

@ -79,7 +79,6 @@ void interpretRC()
}
armed = controls[RC_CHANNEL_THROTTLE] >= 0.05 && controls[RC_CHANNEL_AUX] >= 0.5;
thrustTarget = controls[RC_CHANNEL_THROTTLE];
if (mode == ACRO) {

14
flix/imu.ino
View File

@ -38,7 +38,7 @@ void setupIMU()
bool readIMU()
{
if (IMU.readSensor() < 0) {
Serial.println("IMU read error"); // TODO:
Serial.println("IMU read error");
return false;
}
@ -83,6 +83,7 @@ void calibrateAccel()
void loadAccelCal()
{
// NOTE: this should be changed to the actual values
IMU.setAccelCalX(-0.0048542023, 1.0008112192);
IMU.setAccelCalY(0.0521845818, 0.9985780716);
IMU.setAccelCalZ(0.5754694939, 1.0045746565);
@ -90,6 +91,7 @@ void loadAccelCal()
void loadGyroCal()
{
// NOTE: this should be changed to the actual values
IMU.setGyroBiasX_rads(-0.0185128022);
IMU.setGyroBiasY_rads(-0.0262369743);
IMU.setGyroBiasZ_rads(0.0163032326);
@ -101,13 +103,3 @@ void printIMUCal()
Serial.printf("accel bias: %f %f %f\n", IMU.getAccelBiasX_mss(), IMU.getAccelBiasY_mss(), IMU.getAccelBiasZ_mss());
Serial.printf("accel scale: %f %f %f\n", IMU.getAccelScaleFactorX(), IMU.getAccelScaleFactorY(), IMU.getAccelScaleFactorZ());
}
// Accel bias: 0.0463809967 0.0463809967 0.1486964226
// Accel scale: 0.9986976385 0.9993721247 1.0561490059
// Accel bias: 0.0145006180 0.0145006180 0.0000000000
// Accel scale: 0.9989741445 0.9993283749 1.0000000000
// Correct:
// Accel bias: -0.0048542023 0.0521845818 0.5754694939
// Accel scale: 1.0008112192 0.9985780716 1.0045746565

5
tools/fft.py
View File

@ -34,6 +34,11 @@ N = int(sample_rate * duration)
yf = np.fft.rfft(records)
xf = np.fft.rfftfreq(N, 1 / sample_rate)
# print out fft data
print('Frequency, Amplitude')
for i in range(len(xf)):
print(xf[i], ',', abs(yf[i]))
plt.plot(xf, np.abs(yf))
plt.title('FFT of ' + log_entry)
plt.xlabel('Frequency')