Synthetic UAV Flight Trajectories
A large set of synthetic UAV flight trajectories produced by the RIOTU Lab's automated ROS 2 / Gazebo / PX4 pipeline (FlightGen). A simulated multirotor tracks time-parameterized reference paths — circles and lemniscates with randomized centres, radii and angular velocities — while its ground-truth 3D position is logged and resampled at 10 Hz. Each trajectory is a standalone CSV of `timestamp, tx, ty, tz`; it was built as training data for the VECTOR GRU trajectory-prediction model. *This is a narrow, low-richness dataset: pure position traces of two analytic path shapes, with no imagery, no other sensors, no actions and no failures. Useful for trajectory prediction, not for robot-learning. The paper claims 5,375 trajectories and 20 h; the actual release contains 5,093 CSVs measuring 21.3 h — the figures here are measured from the released data. The sample images are rendered from the real CSVs, since the dataset contains no imagery of its own.*
- Scale
- 21.28 hours
- Formats
- custom
- License
- Apache-2.0
- Published
- 2024-02-15
Decision summary
scripted
Metadata requires review against the official source before publication.
Read the dataset manifest and feature schema.
Catalog assessment
Selection evidence
70/100
Provisional · confidence 35 · 2/6 evaluated
Access and governance
Access and license are declared by the source.
Schema and signal coverage
No machine-readable schema has been verified yet.
Policy training readiness
Observation/action alignment has not been established.
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World-model readiness
World-model observation, geometry, or temporal semantics are not verified.
Failure and recovery readiness
No verified failure/recovery annotation evidence is available yet.
Download and processing readiness
Scale is declared; transfer and processing estimates are not measured.
Review unresolved evidence and next checks
Signal gaps
- Language intent / task phase. Not enough evidence is available to classify this signal.
Next checks
- Read the dataset manifest and feature schema.
- Run a bounded sample audit before assigning Strong readiness.
Record specifics
Dataset facts
- Source
- RIOTU Lab, Prince Sultan University
- Evidence
- secondary claim
- Formats
- custom
- episodes
- 5093
- hours
- 21.28
- tasks
- 2
- bytes
- 57615829
Metadata coverage
Loop signals
6/7 present or partial
No decision-grade evidence captured yet.
unknownView 6 more signal categories
A large set of synthetic UAV flight trajectories produced by the RIOTU Lab's automated ROS 2 / Gazebo / PX4 pipeline (FlightGen). A simulated multirotor tracks time-parameterized reference paths — circles and lemniscates with randomized centres, radii and angular velocities — while its ground-truth 3D position is logged and resampled at 10 Hz. Each trajectory is a standalone CSV of `timestamp, tx, ty, tz`; it was built as training data for the VECTOR GRU trajectory-prediction model. *This is a narrow, low-richness dataset: pure position traces of two analytic path shapes, with no imagery, no other sensors, no actions and no failures. Useful for trajectory prediction, not for robot-learning. The paper claims 5,375 trajectories and 20 h; the actual release contains 5,093 CSVs measuring 21.3 h — the figures here are measured from the released data. The sample images are rendered from the real CSVs, since the dataset contains no imagery of its own.*
partialA large set of synthetic UAV flight trajectories produced by the RIOTU Lab's automated ROS 2 / Gazebo / PX4 pipeline (FlightGen). A simulated multirotor tracks time-parameterized reference paths — circles and lemniscates with randomized centres, radii and angular velocities — while its ground-truth 3D position is logged and resampled at 10 Hz. Each trajectory is a standalone CSV of `timestamp, tx, ty, tz`; it was built as training data for the VECTOR GRU trajectory-prediction model. *This is a narrow, low-richness dataset: pure position traces of two analytic path shapes, with no imagery, no other sensors, no actions and no failures. Useful for trajectory prediction, not for robot-learning. The paper claims 5,375 trajectories and 20 h; the actual release contains 5,093 CSVs measuring 21.3 h — the figures here are measured from the released data. The sample images are rendered from the real CSVs, since the dataset contains no imagery of its own.*
presentA large set of synthetic UAV flight trajectories produced by the RIOTU Lab's automated ROS 2 / Gazebo / PX4 pipeline (FlightGen). A simulated multirotor tracks time-parameterized reference paths — circles and lemniscates with randomized centres, radii and angular velocities — while its ground-truth 3D position is logged and resampled at 10 Hz. Each trajectory is a standalone CSV of `timestamp, tx, ty, tz`; it was built as training data for the VECTOR GRU trajectory-prediction model. *This is a narrow, low-richness dataset: pure position traces of two analytic path shapes, with no imagery, no other sensors, no actions and no failures. Useful for trajectory prediction, not for robot-learning. The paper claims 5,375 trajectories and 20 h; the actual release contains 5,093 CSVs measuring 21.3 h — the figures here are measured from the released data. The sample images are rendered from the real CSVs, since the dataset contains no imagery of its own.*
presentA large set of synthetic UAV flight trajectories produced by the RIOTU Lab's automated ROS 2 / Gazebo / PX4 pipeline (FlightGen). A simulated multirotor tracks time-parameterized reference paths — circles and lemniscates with randomized centres, radii and angular velocities — while its ground-truth 3D position is logged and resampled at 10 Hz. Each trajectory is a standalone CSV of `timestamp, tx, ty, tz`; it was built as training data for the VECTOR GRU trajectory-prediction model. *This is a narrow, low-richness dataset: pure position traces of two analytic path shapes, with no imagery, no other sensors, no actions and no failures. Useful for trajectory prediction, not for robot-learning. The paper claims 5,375 trajectories and 20 h; the actual release contains 5,093 CSVs measuring 21.3 h — the figures here are measured from the released data. The sample images are rendered from the real CSVs, since the dataset contains no imagery of its own.*
presentsimulation · A large set of synthetic UAV flight trajectories produced by the RIOTU Lab's automated ROS 2 / Gazebo / PX4 pipeline (FlightGen). A simulated multirotor tracks time-parameterized reference paths — circles and lemniscates with randomized centres, radii and angular velocities — while its ground-truth 3D position is logged and resampled at 10 Hz. Each trajectory is a standalone CSV of `timestamp, tx, ty, tz`; it was built as training data for the VECTOR GRU trajectory-prediction model. *This is a narrow, low-richness dataset: pure position traces of two analytic path shapes, with no imagery, no other sensors, no actions and no failures. Useful for trajectory prediction, not for robot-learning. The paper claims 5,375 trajectories and 20 h; the actual release contains 5,093 CSVs measuring 21.3 h — the figures here are measured from the released data. The sample images are rendered from the real CSVs, since the dataset contains no imagery of its own.*
presentOpen · Apache-2.0 · custom
partialEvidence details and provenance
Official signal claims
Schema and annotations
No machine-readable schema facts are captured.
Sample verification
Pending. Metadata does not prove sample coverage, alignment, or file integrity.
curated official source
Integration notes
- Metadata requires review against the official source before publication.
Catalog links