---
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).