Datasets:
File size: 2,721 Bytes
d707571 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 | ---
license: mit
task_categories:
- robotics
- time-series-forecasting
language:
- en
tags:
- tsfile
- timeseries
- robotics
- embedded-systems
- ultrasonic
- autonomous-robot
- PIC16F877A
- time-series
- format:tsfile
pretty_name: Robot Sensor Telemetry Dataset (TsFile)
size_categories:
- n<1K
configs:
- config_name: default
data_files:
- split: robot_sensor_telemetry
path: robot_sensor_telemetry.tsfile
---
# Robot Sensor Telemetry Dataset (TsFile)
Apache TsFile version of [`nuerrr/robot-sensor-telemetry`](https://huggingface.co/datasets/nuerrr/robot-sensor-telemetry).
## Overview
This dataset contains real-time sensor telemetry collected from an autonomous robot built for CIE-349/408 Embedded Systems at Zewail City University of Science and Technology (Spring 2026).
- **Tables:** 1 TsFile table(s), 81 rows in total.
- `robot_sensor_telemetry`: 81 rows, 1970-01-01 00:00:00 → 1970-01-01 00:00:20 (UTC)
## Schema (TsFile structure)
### `robot_sensor_telemetry.tsfile`
- **Time** (INT64, milliseconds) — the source `timestamp_ms` column.
- **TAG**: none — the table is stored as a single device.
- **FIELD** (8): `front_cm` (int64), `rear_cm` (int64), `left_cm` (int64), `right_cm` (int64), `motor_state` (string), `face_verified` (int64), `tilt_warning` (int64), `fall_detected` (int64)
## Conversion notes
- One CSV, one TsFile, single robot: no TAG column.
- Time = the source `timestamp_ms` (milliseconds since the telemetry session started, 250 ms sampling) stored as INT64 milliseconds.
- No column is dropped.
## Usage
Install the Apache TsFile Python SDK (`pip install tsfile`) and read a converted file:
```python
from pathlib import Path
from tsfile import TsFileReader
path = Path("robot_sensor_telemetry.tsfile")
with TsFileReader(str(path)) as reader:
schemas = reader.get_all_table_schemas()
print("tables:", list(schemas))
table_name = next(iter(schemas))
table = schemas[table_name]
columns = [column.get_column_name() for column in table.get_columns()]
print("columns:", columns)
field_names = [
column.get_column_name()
for column in table.get_columns()
if column.get_column_name() not in {"Time", "time"}
]
if field_names:
with reader.query_table(table_name, field_names[:3], batch_size=1024) as result:
batch = result.read_arrow_batch()
if batch is not None:
print(batch.to_pandas().head())
```
## Source & license
- Original dataset: [`nuerrr/robot-sensor-telemetry`](https://huggingface.co/datasets/nuerrr/robot-sensor-telemetry)
- Source card title / publisher: Robot Sensor Telemetry Dataset
- License: mit (as declared by the original dataset).
|