README.md

metadata

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.

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:

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
• Source card title / publisher: Robot Sensor Telemetry Dataset
• License: mit (as declared by the original dataset).