Data
wall-robot-navigation

wall-robot-navigation

active ARFF 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 Ceará Fortaleza, Ceará, 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), Valparaíso-Chile, pages 1-6, DOI: 10.1109/LARS.2009.5418323

5 features

Class (target)nominal4 unique values
0 missing
V1numeric1687 unique values
0 missing
V2numeric837 unique values
0 missing
V3numeric1709 unique values
0 missing
V4numeric1779 unique values
0 missing

62 properties

5456
Number of instances (rows) of the dataset.
5
Number of attributes (columns) of the dataset.
4
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
4
Number of numeric attributes.
1
Number of nominal attributes.
0
Percentage of binary attributes.
0.59
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
0.49
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
38.07
Maximum kurtosis among attributes of the numeric type.
0.68
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
30.32
Third quartile of kurtosis among attributes of the numeric type.
1.88
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
80
Percentage of numeric attributes.
1.73
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
4
The minimal number of distinct values among attributes of the nominal type.
20
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
4
The maximum number of distinct values among attributes of the nominal type.
0.96
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
4.37
Third quartile of skewness among attributes of the numeric type.
5.01
Maximum skewness among attributes of the numeric type.
0.34
Minimum standard deviation of attributes of the numeric type.
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.
0.82
Maximum standard deviation of attributes of the numeric type.
6.01
Percentage of instances belonging to the least frequent class.
0.83
First quartile of means among attributes of the numeric type.
0
Standard deviation of the number of distinct values among attributes of the nominal type.
Average entropy of the attributes.
328
Number of instances belonging to the least frequent class.
First quartile of mutual information between the nominal attributes and the target attribute.
12.36
Mean kurtosis among attributes of the numeric type.
0
Number of binary attributes.
1.15
First quartile of skewness among attributes of the numeric type.
1.28
Mean of means among attributes of the numeric type.
0.4
First quartile of standard deviation of attributes of the numeric type.
0.93
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
1.71
Entropy of the target attribute values.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
5.43
Second quartile (Median) of kurtosis among attributes of the numeric type.
0
Number of attributes divided by the number of instances.
4
Average number of distinct values among the attributes of the nominal type.
1.28
Second quartile (Median) of means among attributes of the numeric type.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
2.54
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
40.41
Percentage of instances belonging to the most frequent class.
0.59
Mean standard deviation of attributes of the numeric type.
2.09
Second quartile (Median) of skewness among attributes of the numeric type.
2205
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.

5 tasks

107 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
31 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Class
0 runs - estimation_procedure: 50 times Clustering
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