These files can be used to convert flight data files from Parrot Disco drone to KML and CSV format.
Connect to the Bebop's / Disco's WiFi network, then:
$ telnet 192.168.42.1
There's an FTP server running on the normal port with no username or password, which provides access to everything under /data/ftp, which includes media (JPEGs, DNGs, videos), PUDs, logs, black box recordings and more.
The drone and controller seem to interact via two ports:
c2dport: 54321
d2cport: 43210
To active USB networking:
/ # /bin/usbnetwork.sh
The drone's IP address on the USB network is 192.168.43.1
Short pressing on the drone's button while USB connected might also activate USB networking (untested).
/data/dragon.conf contains a JSON dict with many settings, including blackbox_enable and navdata_enable.
/etc/debug.conf: contains debug settings, including enabling blackbox and navdata.
/etc/gps_config.txt: contains the GPS configuration:
PERDAPI,FIRSTFIXFILTER,STRONG
PERDAPI,FIXPERSEC,5
PERDAPI,FIXMASK,SENSITIVITY
PERDAPI,STATIC,0,0
PERDAPI,LATPROP,-1
PERDAPI,OUTPROP,0
PERDAPI,CROUT,V
PERDAPI,PIN,OFF
PERDAPI,GNSS,AUTO,2,2,0,-1,-1
PERDSYS,ANTSEL,FORCE1L
To monitor GPS output (NMEA):
/ # cat `cat /etc/parrot/gps/tty`
You'll see one NMEA stanza per second, for example:
$GNGST,000608.916,,,,,,,*57
$GNGSA,A,1,,,,,,,,,,,,,,,,1*1D
$GNZDA,000609.029,22,08,1999,,*4C
$GPGSV,1,1,00,,,,,,,,,,,,,,,,,1*64
$GLGSV,1,1,00,,,,,,,,,,,,,,,,,1*78
$PERDCRV,0.00,0,0.00,0.00,0.00,44.0,13368000*49
$GNRMC,000609.916,V,0000.0000,N,00000.0000,E,0.00,0.00,220899,,,N,V*1B
$GNGNS,000609.916,0000.0000,N,00000.0000,E,NNN,00,,-18.0,18.0,,,V*6E
/usr/bin # ./p7_sensors-test
posix init start build on : Nov 13 2014 19:27:05
Use ctrl+\ (SIGQUIT) to end the application
Usage: ./p7_sensors-test [options]
Options:
-h | --help Print this message
-b | --bit-mask Bitfield to activate sensors
> 00000001 : vertical camera................ mt9v117
> 00000010 : gyro/accelero.................. mpu6050
> 00000100 : pressure/temperature sensor.... ms5607
> 00001000 : magneto sensor................. ak8963
> 00010000 : us sensor...................... xxxxxxx
> 00100000 : gyro in fifo................... mpu6050
> 01000000 : accelero in fifo............... mpu6050
> 10000000 : fifo count..................... mpu6050
-s | --samples Number of acquisition (default = 5000)
-d | --debug-print Debug print
-f | --file Log file
-p | --port Network port to use-H Human friendly (print matrices on several lines)
-M Machine friendly (all data on one line)
-m if us is activated > 1 : low_power mode > 0 : high_power mode
The drone records every flight as a single file in /data/ftp/internal_000/Bebop_Drone/academy.
on Disco it's /data/ftp/internal_000/Academy/. PUD files are recorded only when video is recorded.
The file format is self-describing. Each file begins with a null-terminated JSON string listing the columns present in each data packet. For example (pretty-printed here for clarity):
{
"version": "1.0",
"date": "2014-11-30T160423+0000",
"product_id": 2305,
"serial_number": "PI...",
"uuid": "EB...",
"controller_model": "manta",
"controller_application": "Nexus 10",
"run_origin": 0,
"details_headers": [
{
"name": "time",
"type": "integer",
"size": 4
},
{
"name": "battery_level",
"type": "integer",
"size": 4
},
{
"name": "controller_gps_longitude",
"type": "double",
"size": 8
},
{
"name": "controller_gps_latitude",
"type": "double",
"size": 8
}, ...
]
}
This JSON header is followed by fixed-size binary packets, through to the end of the file. There are roughly 30 packets per second.
The fields currently present in the log packets are:
| Name | Type | Size | Description |
|---|---|---|---|
| time | integer | 4 | Timestamp of the log entry, in milliseconds |
| battery_level | integer | 4 | Battery level, in percent |
| controller_gps_longitude | double | 8 | Controller GPS longitude, in degrees |
| controller_gps_latitude | double | 8 | Controller GPS latitude, in degrees |
| flying_state | integer | 1 | Flying state: 1 = landed, 2 = in the air, 3 = in the air |
| alert_state | integer | 1 | Alert state: 0 = normal |
| wifi_signal | integer | 1 | WiFi signal strength, always 0 right now |
| product_gps_available | boolean | 1 | Drone GPS availability, always 0 right now |
| product_gps_longitude | double | 8 | Drone GPS longitude, in degrees |
| product_gps_latitude | double | 8 | Drone GPS latitude, in degrees |
| product_gps_position_error | integer | 4 | Drone GPS position error, always 0 right now |
| speed_vx | float | 4 | Horizontal speed, unknown units |
| speed_vy | float | 4 | Horizontal speed, unknown units |
| speed_vz | float | 4 | Vertical speed, unknown units |
| angle_phi | float | 4 | Euler angle phi, likely in radians |
| angle_theta | float | 4 | Euler angle theta, likely in radians |
| angle_psi | float | 4 | Euler angle psi, likely in radians |
| altitude | integer | 4 | Altitude, likely in centimeters |
| flip_type | integer | 1 | Flip type, 0 = no flip |
A quick way to dump the data as a table from the shell is to run:
hexdump -s 1379 -e ' "%07_ad|" 2/4 "%8d" 2/8 "%13.7f" 4/1 "%2d" 2/8 "%13.7f " 1/4 "%4d" 6/4 "%12.5f" 1/4 "%6d" 1/1 "%3d" "\n" ' *.pud | more
This repository contains a Python script to convert .pud files into .csv and .kml files (for Google Earth). For .kml support, you'll need the simplekml package, which can be installed trivially using easy_install simplekml. To convert a single .pud file:
./pud_to_csv_kml.py 0901_2014-12-01T162824+0000_F88751.pud
To convert all the .pud files in a directory:
./pud_to_csv_kml.py -d /path/to/directory
Run /usr/bin/DragonDebug.sh to enable debug mode.
To turn on blackbox recordings, edit /etc/debug.conf and set the blackbox flag to true. The Bebop will then write one file per flight to /data/ftp/internal_000/blackbox. The files are named light_run_*. The format is as follows.
The files start with an ASCII header, which contains two sections. The first records the firmware version:
-- Build infos
product: BebopDrone
name: BebopDrone-K...
version: 1.32.0
date: 2014-11-14
time: 10h35m59s
compiler: marjoriecoulin
The second lists the columns present in the recording:
-- Navdata infos
nentries: 129
datasize: 8
titles: index, time_s, sensor_acc_raw_x_m_s2, sensor_acc_raw_y_m_s2, sensor_acc_raw_z_m_s2, sensor_gyro_raw_x_rad_s, sensor_gyro_raw_y_rad_s, sensor_gyro_raw_z_rad_s, sensor_mag_raw_x_mG, sensor_mag_raw_y_mG, sensor_mag_raw_z_mG, phi_EST_rad, theta_EST_rad, psi_EST_rad, gyro_filt_x_rad_s, gyro_filt_y_rad_s, gyro_filt_z_rad_s, p_EST_rad_s, q_EST_rad_s, r_EST_rad_s, acc_x_EST_m_s2, acc_y_EST_m_s2, acc_z_EST_m_s2, speed_horiz_x_m_s, speed_horiz_y_m_s, speed_horiz_z_m_s, sensor_ultrasound_height_m, sensor_pressure_Pa, height_EST_m, height_vision_m, sensor_vision_speed_x_m_s, sensor_vision_speed_y_m_s, sensor_vision_speed_z_m_s, phi_REF_rad, theta_REF_rad, psi_REF_rad, p_REF_rad_s, q_REF_rad_s, r_REF_rad_s, r_wanted_rad_s, motor_cmd_pitch, motor_cmd_roll, motor_cmd_yaw, height_REF_m, height_REF_filt_m, speed_z_REF_m_s, motor_cmd_height, motor_cmd_ff, motor_cmd_1_rpm, motor_cmd_2_rpm, motor_cmd_3_rpm, motor_cmd_4_rpm, controler_state, acc_bias_x_m_s2, acc_bias_y_m_s2, acc_bias_z_m_s2, gyro_bias_x_rad_s, gyro_bias_y_rad_s, gyro_bias_z_rad_s, gyro_unbias_x_rad_s, gyro_unbias_y_rad_s, gyro_unbias_z_rad_s, speed_body_x_m_s, speed_body_y_m_s, speed_body_z_m_s, sensor_imu_ref_temperature_degC, sensor_imu_obs_temperature_degC, sensor_barometer_temperature_degC, battery_dV, motor1_obs_speed_rpm, motor2_obs_speed_rpm, motor3_obs_speed_rpm, motor4_obs_speed_rpm, BLDC_error, BLDC_motors_fault, BLDC_status, BLDC_temperature_degC, calage_x_rad, calage_y_rad, biais_pression_m, use_US, estimator_drone_position_m_x, estimator_drone_position_m_y, estimator_drone_position_m_z, estimator_psi_fused_rad, sensor_ultrasound_id, vision_indicator, sensor_ultrasound_mode, magneto_bias_x, magneto_bias_y, magneto_bias_z, magneto_radius, sensor_gps_flags, sensor_gps_latitude_deg, sensor_gps_longitude_deg, sensor_gps_altitude_m, sensor_gps_speed_m_s, sensor_gps_bearing_deg, sensor_gps_accuracy, sensor_gps_num_svs, sensor_gps_used_in_fix_mask, heading_magneto_rad, magneto_calibration_state, altitude_pression_m, altitude_pression_filt_m, dynamic_model_b, dynamic_model_f0, dynamic_model_Cz, dynamic_model_rpm_eq, psi_VIDEOREF_rad, airspeed_body_x_m_s, airspeed_body_y_m_s, airspeed_body_z_m_s, wind_body_x_m_s, wind_body_y_m_s, wind_body_z_m_s, stateFlightPlan, gpsDeviationPostionErrorLat_m, gpsDeviationPostionErrorLong_m, gpsDeviationPostionErrorAlt_m, gpsLatitudeRelative_m, gpsLongitudeRelative_m, gpsNorthSpeed_m_s, gpsEstSpeed_m_s, gpsDataOk, gpsNewValidData, battery_filt, magneticDeclination_rad, magneticDeclinationLocked
There are currently 129 columns in the recordings, everything from raw sensor data to attitude, position and wind estimates, to motor commands.
The data follows a data header:
-- Data
Each packet contains nentries double floating point values, each datasize (8) bytes long.
The packets are logged at a rate of 200Hz.
This repository contains a Python script to convert blackbox files into .csv files. To convert a single blackbox file:
./blackbox_to_csv.py light_run_0
To convert all the .pud files in a directory:
./blackbox_to_csv.py -d /path/to/directory