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wall-robot-navigation

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Publicly available Visibility: public Uploaded 01-06-2015 by Rafael G. Mantovani

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Author: Ananda Freire, Marcus Veloso and Guilherme Barreto
Source: [original](http://www.openml.org/d/1497) - UCI
Please cite:
* Dataset Title:
Wall-Following Robot Navigation Data Data Set (version with 4 Attributes)
* Abstract:
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'.
* Source:
(a) Creators: Ananda Freire, Marcus Veloso and Guilherme Barreto
Department of Teleinformatics Engineering
Federal University of CearÃ¡
Fortaleza, CearÃ¡, Brazil
(b) Donors of database: Ananda Freire (anandalf '@' gmail.com)
Guilherme Barreto (guilherme '@' deti.ufc.br)
* Data Set Information:
The provided file contain the 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.
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.
If some kind of short-term memory mechanism is provided to the neural classifiers, their performances are improved in general. For example, if past inputs are provided together with current sensor readings, even the Perceptron becomes able to learn the task and command the robot successfully. If a recurrent neural network, such as the Elman network, is used to learn the task, the resulting dynamical classifier is able to learn the task using less hidden neurons than the MLP network.
* Attribute Information:
Number of Attributes: sensor_readings_24.data: 24 numeric attributes and the class.
For Each Attribute:
-- File sensor_readings_4.data:
1. SD_front: minimum sensor reading within a 60 degree arc located at the front of the robot - (numeric: real)
2. SD_left: minimum sensor reading within a 60 degree arc located at the left of the robot - (numeric: real)
3. SD_right: minimum sensor reading within a 60 degree arc located at the right of the robot - (numeric: real)
4. SD_back: minimum sensor reading within a 60 degree arc located at the back of the robot - (numeric: real)
5. Class: {Move-Forward, Slight-Right-Turn, Sharp-Right-Turn, Slight-Left-Turn}
* 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),
ValparaÃso-Chile, pages 1-6, DOI: 10.1109/LARS.2009.5418323

Class (target) | nominal | 4 unique values 0 missing | |

V1 | numeric | 1687 unique values 0 missing | |

V2 | numeric | 837 unique values 0 missing | |

V3 | numeric | 1709 unique values 0 missing | |

V4 | numeric | 1779 unique values 0 missing |

1

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3

0.87

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump

1.28

Second quartile (Median) of means among attributes of the numeric type.

0.21

Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump

Second quartile (Median) of mutual information between the nominal attributes and the target attribute.

1

Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3

0.65

Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump

Minimal mutual information between the nominal attributes and the target attribute.

2.09

Second quartile (Median) of skewness among attributes of the numeric type.

1

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1

Maximum mutual information between the nominal attributes and the target attribute.

4

The minimal number of distinct values among attributes of the nominal type.

0.59

Second quartile (Median) of standard deviation of attributes of the numeric type.

0

Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1

Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.

4

The maximum number of distinct values among attributes of the nominal type.

1

Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1

1

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001

30.32

Third quartile of kurtosis among attributes of the numeric type.

1

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

1

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2

0

Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

0

Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2

Third quartile of mutual information between the nominal attributes and the target attribute.

1

Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

1

Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2

1

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001

0.97

Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes

4.37

Third quartile of skewness among attributes of the numeric type.

1

Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

1

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3

1.31

First quartile of kurtosis among attributes of the numeric type.

0.77

Third quartile of standard deviation of attributes of the numeric type.

1

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1

0

Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

0

Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3

Average mutual information between the nominal attributes and the target attribute.

1

Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

1

Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3

1

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001

An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.

First quartile of mutual information between the nominal attributes and the target attribute.

1

Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1

1

Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

0

Standard deviation of the number of distinct values among attributes of the nominal type.

4

Average number of distinct values among the attributes of the nominal type.

1.15

First quartile of skewness among attributes of the numeric type.

1

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2

0

Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

0.4

First quartile of standard deviation of attributes of the numeric type.

1

Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

1

Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2

5.43

Second quartile (Median) of kurtosis among attributes of the numeric type.