{"url":"/dataset/data-collected-with-package-delivery","name":"Data Collected with Package Delivery Quadcopter Drone","full_name":null,"description_markdown":"This experiment was performed in order to empirically measure the energy use of small, electric Unmanned Aerial Vehicles (UAVs). We autonomously direct a DJI ® Matrice 100 (M100) drone to take off, carry a range of payload weights on a triangular flight pattern, and land. Between flights, we varied specified parameters through a set of discrete options, payload of 0 , 250 g and 500 g; altitude during cruise of 25 m, 50 m, 75 m and 100 m; and speed during cruise of 4 m/s, 6 m/s, 8 m/s, 10 m/s and 12 m/s.\r\n\r\nWe simultaneously collect data from a broad array of on-board sensors. The onboard sensors used to collect these data are\r\n\r\n* Wind sensor: FT Technologies FT205 UAV-mountable, pre-calibrated ultrasonic wind sensor with accuracy of $\\pm$ 0.1 m/s and refresh rate of 10 Hz.;\r\n\r\n* Position: 3DM-GX5-45 GNSS/INS sensor pack. These sensors use a built-in Kalman filtering system to fuse the GPS and IMU data. The sensor has a maximum output rate of 10Hz with accuracy of $\\pm$2 m RMS horizontal, $\\pm$5 m RMS vertical.\r\n\r\n* Current and Voltage: Mauch Electronics PL-200 sensor. This sensor can record currents up to 200 A and voltages up to 33 V. Analogue readings from the sensor were converted into a digital format using an 8 channel 17 bit analogue-to-digital converter (ADC).\r\n\r\nThe number of flights performed varying operational parameters (payload, altitude, speed) was 196. In addition, 13 recordings were done to assess the drone’s ancillary power and hover conditions.","description_withheld":null,"homepage":"https://doi.org/10.1184/R1/12683453.v1","introduced_date":"2020-07-27","introduced_date_note":null,"introduced_by":{"paper":null,"title":"In-flight positional and energy use data set of a DJI Matrice 100 quadcopter for small package delivery","first_author":null,"url":null},"license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"modalities":[{"name":"Tabular","url":"/datasets/modality/tabular"},{"name":"Time series","url":"/datasets/modality/time-series"}],"tasks":[{"name":"Multivariate Time Series Forecasting","url":"/task/multivariate-time-series-forecasting","datasets_with_task":"/datasets/task/multivariate-time-series-forecasting"},{"name":"Time Series Prediction","url":"/task/time-series-prediction","datasets_with_task":"/datasets/task/time-series-prediction"},{"name":"Model extraction","url":"/task/model-extraction","datasets_with_task":"/datasets/task/model-extraction"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["Data Collected with Package Delivery Quadcopter Drone"],"data_loaders":[],"num_papers_in_archive":2,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/time-series-prediction-on-data-collected-with","task":"Time Series Prediction","dataset_variant":"Data Collected with Package Delivery Quadcopter Drone","rows":1,"metrics":["Average mean absolute error"],"first_row_in_archive_order":{"model":"CMU-DEM","paper":"/paper/cvar-based-flight-energy-risk-assessment-for","metrics":{"Average mean absolute error":"9.06"},"code_links":[{"title":"castacks/cvar-energy-risk-deep-model","url":"https://github.com/castacks/cvar-energy-risk-deep-model"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/cvar-based-flight-energy-risk-assessment-for","title":"CVaR-based Flight Energy Risk Assessment for Multirotor UAVs using a Deep Energy Model","date":"2021-05-31","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}