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wall-robot-navigation

wall-robot-navigation

active ARFF Publicly available Visibility: public Uploaded 25-05-2015 by Rafael G. Mantovani
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  • OpenML-CC18 OpenML100 study_123 study_14 study_34 study_52 study_7 study_98 study_99
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Author: Ananda Freire, Marcus Veloso and Guilherme Barreto Source: [UCI](https://archive.ics.uci.edu/ml/datasets/Wall-Following+Robot+Navigation+Data) - 2010 Please cite: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html) 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: 1. Move-Forward, 2. Slight-Right-Turn, 3. Sharp-Right-Turn, 4. 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: 1. US1: ultrasound sensor at the front of the robot (reference angle: 180°) 2. US2: ultrasound reading (reference angle: -165°) 3. US3: ultrasound reading (reference angle: -150°) 4. US4: ultrasound reading (reference angle: -135°) 5. US5: ultrasound reading (reference angle: -120°) 6. US6: ultrasound reading (reference angle: -105°) 7. US7: ultrasound reading (reference angle: -90°) 8. US8: ultrasound reading (reference angle: -75°) 9. US9: ultrasound reading (reference angle: -60°) 10. US10: ultrasound reading (reference angle: -45°) 11. US11: ultrasound reading (reference angle: -30°) 12. US12: ultrasound reading (reference angle: -15°) 13. US13: reading of ultrasound sensor situated at the back of the robot (reference angle: 0°) 14. US14: ultrasound reading (reference angle: 15°) 15. US15: ultrasound reading (reference angle: 30°) 16. US16: ultrasound reading (reference angle: 45°) 17. US17: ultrasound reading (reference angle: 60°) 18. US18: ultrasound reading (reference angle: 75°) 19. US19: ultrasound reading (reference angle: 90°) 20. US20: ultrasound reading (reference angle: 105°) 21. US21: ultrasound reading (reference angle: 120°) 22. US22: ultrasound reading (reference angle: 135°) 23. US23: ultrasound reading (reference angle: 150°) 24. 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

25 features

Class (target)nominal4 unique values
0 missing
V1numeric1977 unique values
0 missing
V2numeric2034 unique values
0 missing
V3numeric1786 unique values
0 missing
V4numeric1767 unique values
0 missing
V5numeric1822 unique values
0 missing
V6numeric1828 unique values
0 missing
V7numeric1530 unique values
0 missing
V8numeric2068 unique values
0 missing
V9numeric1870 unique values
0 missing
V10numeric2003 unique values
0 missing
V11numeric1873 unique values
0 missing
V12numeric1797 unique values
0 missing
V13numeric1570 unique values
0 missing
V14numeric1487 unique values
0 missing
V15numeric1465 unique values
0 missing
V16numeric1295 unique values
0 missing
V17numeric1083 unique values
0 missing
V18numeric971 unique values
0 missing
V19numeric1042 unique values
0 missing
V20numeric1136 unique values
0 missing
V21numeric1355 unique values
0 missing
V22numeric1736 unique values
0 missing
V23numeric1758 unique values
0 missing
V24numeric1856 unique values
0 missing

62 properties

5456
Number of instances (rows) of the dataset.
25
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.
24
Number of numeric attributes.
1
Number of nominal attributes.
0
Percentage of binary attributes.
1.29
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-1.6
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
14.35
Maximum kurtosis among attributes of the numeric type.
0.91
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
5.98
Third quartile of kurtosis among attributes of the numeric type.
3.35
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
96
Percentage of numeric attributes.
2.73
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.
4
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.
0.02
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
2.54
Third quartile of skewness among attributes of the numeric type.
3.83
Maximum skewness among attributes of the numeric type.
0.8
Minimum standard deviation of attributes of the numeric type.
-0.97
First quartile of kurtosis among attributes of the numeric type.
1.4
Third quartile of standard deviation of attributes of the numeric type.
1.72
Maximum standard deviation of attributes of the numeric type.
6.01
Percentage of instances belonging to the least frequent class.
1.27
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.
2.37
Mean kurtosis among attributes of the numeric type.
0
Number of binary attributes.
0.72
First quartile of skewness among attributes of the numeric type.
2.05
Mean of means among attributes of the numeric type.
1.12
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.
-0.08
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.
2.16
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.
1.52
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.
1.25
Mean standard deviation of attributes of the numeric type.
1.18
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.

24 tasks

11643 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
32 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: 33% Holdout set - target_feature: Class
43 runs - estimation_procedure: 10-fold Learning Curve - target_feature: Class
0 runs - target_feature: Class
1303 runs - target_feature: Class
1302 runs - target_feature: Class
1301 runs - target_feature: Class
1298 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
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