Data
wall-robot-navigation

wall-robot-navigation

active ARFF Publicly available Visibility: public Uploaded 25-05-2015 by Rafael G. Mantovani
0 likes downloaded by 0 people , 0 total downloads 0 issues 0 downvotes
  • study_14 study_1 study_1299 study_6748 study_11388 study_5693 study_1524 study_12449 study_5015 study_7240 study_12392 study_1011 study_2056 study_2728 study_2907 study_12215 study_3484 study_4051 study_11606 study_11623 study_12622 study_862 study_3399 study_6054 study_10740 study_11460 study_12392 study_5972 study_7020 study_7452 study_11684 study_4677 study_4812 study_11643 study_13008 study_638 study_308 study_1419 study_7346 study_12215 study_695 study_1953 study_2480 study_4563 study_6600 study_7261 study_10612 study_11409 study_12252
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
Author: Ananda Freire, Marcus Veloso and Guilherme Barreto Source: UCI Please cite: * Dataset Title: Wall-Following Robot Navigation Data Data Set * 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_24.data: 1. US1: ultrasound sensor at the front of the robot (reference angle: 180°) - (numeric: real) 2. US2: ultrasound reading (reference angle: -165°) - (numeric: real) 3. US3: ultrasound reading (reference angle: -150°) - (numeric: real) 4. US4: ultrasound reading (reference angle: -135°) - (numeric: real) 5. US5: ultrasound reading (reference angle: -120°) - (numeric: real) 6. US6: ultrasound reading (reference angle: -105°) - (numeric: real) 7. US7: ultrasound reading (reference angle: -90°) - (numeric: real) 8. US8: ultrasound reading (reference angle: -75°) - (numeric: real) 9. US9: ultrasound reading (reference angle: -60°) - (numeric: real) 10. US10: ultrasound reading (reference angle: -45°) - (numeric: real) 11. US11: ultrasound reading (reference angle: -30°) - (numeric: real) 12. US12: ultrasound reading (reference angle: -15°) - (numeric: real) 13. US13: reading of ultrasound sensor situated at the back of the robot (reference angle: 0°) - (numeric: real) 14. US14: ultrasound reading (reference angle: 15°) - (numeric: real) 15. US15: ultrasound reading (reference angle: 30°) - (numeric: real) 16. US16: ultrasound reading (reference angle: 45°) - (numeric: real) 17. US17: ultrasound reading (reference angle: 60°) - (numeric: real) 18. US18: ultrasound reading (reference angle: 75°) - (numeric: real) 19. US19: ultrasound reading (reference angle: 90°) - (numeric: real) 20. US20: ultrasound reading (reference angle: 105°) - (numeric: real) 21. US21: ultrasound reading (reference angle: 120°) - (numeric: real) 22. US22: ultrasound reading (reference angle: 135°) - (numeric: real) 23. US23: ultrasound reading (reference angle: 150°) - (numeric: real) 24. US24: ultrasound reading (reference angle: 165°) - (numeric: real) 25. 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

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

107 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.
Second quartile (Median) of entropy among attributes.
0.01
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.98
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
0.14
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
40.41
Percentage of instances belonging to the most frequent class.
1.25
Mean standard deviation of attributes of the numeric type.
-0.08
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.99
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1.71
Entropy of the target attribute values.
0.79
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
2205
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
2.16
Second quartile (Median) of means among attributes of the numeric type.
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.78
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
-1.6
Minimum kurtosis among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.01
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.3
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
14.35
Maximum kurtosis among attributes of the numeric type.
0.91
Minimum of means among attributes of the numeric type.
1.18
Second quartile (Median) of skewness among attributes of the numeric type.
0.99
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.5
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
3.35
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of binary attributes.
1.29
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0
Number of attributes divided by the number of instances.
Maximum mutual information between the nominal attributes and the target attribute.
4
The minimal number of distinct values among attributes of the nominal type.
0.02
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.03
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.
0.8
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
5.98
Third quartile of kurtosis among attributes of the numeric type.
0.93
Average class difference between consecutive instances.
0.96
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
3.83
Maximum skewness among attributes of the numeric type.
6.01
Percentage of instances belonging to the least frequent class.
96
Percentage of numeric attributes.
2.73
Third quartile of means among attributes of the numeric type.
0.99
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
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.01
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.72
Maximum standard deviation of attributes of the numeric type.
328
Number of instances belonging to the least frequent class.
4
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.01
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
0.03
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.99
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
2.54
Third quartile of skewness among attributes of the numeric type.
0.98
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
0.96
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
2.37
Mean kurtosis among attributes of the numeric type.
0.47
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-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.
0.99
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
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.01
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
2.05
Mean of means among attributes of the numeric type.
0.36
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1.27
First quartile of means among attributes of the numeric type.
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.01
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
0.03
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.99
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0.01
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.98
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
0.96
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.
0.72
First quartile of skewness among attributes of the numeric type.
0.99
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.99
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.
0.01
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
4
Average number of distinct values among the attributes of the nominal type.
1.12
First 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 2
0.01
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
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.99
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
1.52
Mean skewness among attributes of the numeric type.

11 tasks

10 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: Test on Training Data - target_feature: Class
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 10% Holdout set - target_feature: Class
0 runs - estimation_procedure: 20% Holdout (Ordered) - target_feature: Class
0 runs - estimation_procedure: Leave one out - target_feature: Class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - target_feature: Class
0 runs - estimation_procedure: 10 times 10-fold Learning Curve - target_feature: Class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: Class
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: Class
Define a new task