Zipline operates the world’s only drone delivery system at national scale to send urgent medicines like blood transfusions and vaccines to those in need – no matter where they live. We’re at the forefront of a logistics revolution, designing, manufacturing, and operating our own battery powered autonomous aircraft fleet to overcome the challenges of delivering just-in-time, lifesaving medical supplies around the world.
In this example dataset taken from our distribution center in Muhanga, we serve rural hospitals in the western half of Rwanda, delivering blood transfusions used to treat conditions like malaria anemia and postpartum hemorrhaging. After an order is placed, an electromechanically actuated launcher accelerates one of our aircraft — known internally as a "Zip" — at 5 g's from 0 to 30 m/s (67 mph) in less than a second. From there, the plane autonomously navigates a pre-defined route to the delivery site, drops its package, and returns to the distribution center to recover. If you're looking for more background information on Zipline before diving into the data, you can check out these videos:
- Zipline - Impact in Rwanda - Brief overview of our operations in Rwanda
- How Rwanda Built A Drone Delivery Service - In-depth study of the engineering work behind our current drone platform
- The Super-Fast Logistics of Delivering Blood By Drone - Outline of our delivery services in Rwanda
- In Rwanda, His Drones Are Saving Lives - Profile of one of our first flight operators
- Zipline's World Class Drone Safety Features - A short video describing how we develop safe aircraft
A Zip is made up of three components — a battery, wing, and body. For each flight, a flight operator selects one of each component, "asembling" the Zip immediately prior to flight, as our fulfillment operators prepare the package before it's placed in the assembled Zip.
In this directory, you'll find:
- This readme
- A
flight_XXXXX.csvfile for each flight in the dataset. Each file contains the following signals from 5 seconds prior to launch, to 15 seconds after launch, logged at approximately 50Hz:
| Name | Units | Description |
|---|---|---|
seconds_since_launch |
seconds | time since launch |
position_ned_m[0] |
meters | position of the zip relative to a fixed reference point in the north direction |
position_ned_m[1] |
meters | position of the zip relative to a fixed reference point in the east direction |
position_ned_m[2] |
meters | position of the zip relative to a fixed reference point in the down direction |
velocity_ned_mps[0] |
meters/second | velocity of the zip, in the north direction |
velocity_ned_mps[1] |
meters/second | velocity of the zip, in the east direction |
velocity_ned_mps[2] |
meters/second | velocity of the zip, in the down direction |
accel_body_mps2[0] |
meters/second^2 | acceleration of the zip, in the body-forward direction |
accel_body_mps2[1] |
meters/second^2 | acceleration of the zip, in the body-right direction |
accel_body_mps2[2] |
meters/second^2 | acceleration of the zip, in the body-down direction |
orientation_rad[0] |
radians | Euler (Tait-Bryan) roll of the zip |
orientation_rad[1] |
radians | Euler (Tait-Bryan) pitch of the zip |
orientation_rad[2] |
radians | Euler (Tait-Bryan) yaw of the zip |
angular_rate_body_radps[0] |
radians/second | Angular velocity of the zip, about the body-forward direction |
angular_rate_body_radps[1] |
radians/second | Angular velocity of the zip, about the body-right direction |
angular_rate_body_radps[2] |
radians/second | Angular velocity of the zip, about the body-down direction |
position_sigma_ned_m[0] |
meters | estimated standard error of position_ned_m[0], i.e. positional uncertainty in the north direction |
position_sigma_ned_m[1] |
meters | estimated standard error of position_ned_m[1], i.e. positional uncertainty in the east direction |
position_sigma_ned_m[2] |
meters | estimated standard error of position_ned_m[2], i.e. positional uncertainty in the down direction |
- A
summary_data.csvfile containing the following data for each flight in the dataset:
| Name | Units | Description |
|---|---|---|
flight_id |
n/a | unique identifier for the flight |
battery_serial_number |
n/a | serial number of the battery |
body_serial_number |
n/a | serial number of the body |
wing_serial_number |
n/a | serial number of the wing |
commit |
n/a | git commit SHA representing the version of software |
launch_airspeed |
meters/second | airspeed of the plane during launch |
launch_groundspeed |
meters/second | groundspeed of the plane during launch |
launch_timestamp |
n/a | time string YYYT-MM-DD HH:MM:SS CAT where CAT, i.e. Central Africa Time, is the timezone |
preflight_voltage |
volts | dc voltage of the battery immediately prior to launch |
air_temperature |
celsius | air temperature during launch |
rel_humidity |
percentage | relative humidity during launch |
static_pressure |
pascals | static air pressure during launch |
wind_direction |
degrees | direction of the wind during launch, with 0 blowing to the north, 90 blowing to the east |
wind_magnitude |
meters/second | magnitude of the wind during launch |
This is a relatively unstructured, independent exploratory data analysis & visualization assignment. Use your skills to discern details and find patterns in the data, and show us your process in a Jupyter notebook (or similar). Ultimately, we're looking for you to communicate actionable insights — things like corrupted or missing data points, unexplained behaviors, individual outlier launches, diurnal weather patterns, poorly performing parts, etc. — to the engineering & operations team in the form of interpretable figures and tables with a short description of your findings.
-
Go to terminal/command prompt,
cdto the project folder, and typejupyter notebookto start the jupyter notebook -
On jupyter notebook, click and open the project file:
Zipline_Flights_DataAnalysis.ipynbto inspect the data science & analysis work
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