openml-datasets

name	type	format
V1	number	default
V2	number	default
V3	number	default
V4	number	default
V5	number	default
V6	number	default
V7	number	default
V8	number	default
V9	number	default
V10	number	default
V11	number	default
V12	number	default
V13	number	default
V14	number	default
V15	number	default
V16	number	default
V17	number	default
V18	number	default
V19	number	default
V20	number	default
V21	number	default
V22	number	default
V23	number	default
V24	number	default
Class	number	default

Data Files

File	Description	Size	Last modified	Download
wall-robot-navigation		713 kB	3 months ago	wall-robot-navigation
wall-robot-navigation		712 kB	3 months ago	wall-robot-navigation

Files	Size	Format	Created	Updated	License	Source
2	1.42 MB	arff, csv		about 2 months ago	Open Data Commons Public Domain Dedication and License

The resources for this dataset can be found at https://www.openml.org/d/1497

Author: Ananda Freire, Marcus Veloso and Guilherme Barreto
Source: UCI - 2010
Please cite: UCI

Wall-Following Robot Navigation Data Data Set
The data were collected as the SCITOS G5 robot navigates through the room following the wall in a clockwise direction, for 4 rounds, using 24 ultrasound sensors arranged circularly around its 'waist'.

The data consists of raw values of the measurements of all 24 ultrasound sensors and the corresponding class label. Sensor readings are sampled at a rate of 9 samples per second.

The class labels are:

Move-Forward,
Slight-Right-Turn,
Sharp-Right-Turn,
Slight-Left-Turn

It is worth mentioning that the 24 ultrasound readings and the simplified distances were collected at the same time step, so each file has the same number of rows (one for each sampling time step).

The wall-following task and data gathering were designed to test the hypothesis that this apparently simple navigation task is indeed a non-linearly separable classification task. Thus, linear classifiers, such as the Perceptron network, are not able to learn the task and command the robot around the room without collisions. Nonlinear neural classifiers, such as the MLP network, are able to learn the task and command the robot successfully without collisions.

Attribute Information:

US1: ultrasound sensor at the front of the robot (reference angle: 180°)
US2: ultrasound reading (reference angle: -165°)
US3: ultrasound reading (reference angle: -150°)
US4: ultrasound reading (reference angle: -135°)
US5: ultrasound reading (reference angle: -120°)
US6: ultrasound reading (reference angle: -105°)
US7: ultrasound reading (reference angle: -90°)
US8: ultrasound reading (reference angle: -75°)
US9: ultrasound reading (reference angle: -60°)
US10: ultrasound reading (reference angle: -45°)
US11: ultrasound reading (reference angle: -30°)
US12: ultrasound reading (reference angle: -15°)
US13: reading of ultrasound sensor situated at the back of the robot (reference angle: 0°)
US14: ultrasound reading (reference angle: 15°)
US15: ultrasound reading (reference angle: 30°)
US16: ultrasound reading (reference angle: 45°)
US17: ultrasound reading (reference angle: 60°)
US18: ultrasound reading (reference angle: 75°)
US19: ultrasound reading (reference angle: 90°)
US20: ultrasound reading (reference angle: 105°)
US21: ultrasound reading (reference angle: 120°)
US22: ultrasound reading (reference angle: 135°)
US23: ultrasound reading (reference angle: 150°)
US24: ultrasound reading (reference angle: 165°)

Relevant Papers

Ananda L. Freire, Guilherme A. Barreto, Marcus Veloso and Antonio T. Varela (2009), 'Short-Term Memory Mechanisms in Neural Network Learning of Robot Navigation Tasks: A Case Study'. Proceedings of the 6th Latin American Robotics Symposium (LARS'2009), pages 1-6