1. Title of Database: Wall-Following navigation task with mobile robot SCITOS-G5 2. Sources: (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) (c) Date received: August, 2010 3. Past Usage: (a) 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 4. Relevant Information Paragraph: -- The data were collected as the SCITOS G5 navigates through the room following the wall in a clockwise direction, for 4 rounds. To navigate, the robot uses 24 ultrasound sensors arranged circularly around its "waist". The numbering of the ultrasound sensors starts at the front of the robot and increases in clockwise direction. -- The provided files comprise three diferent data sets. The first one contains the raw values of the measurements of all 24 ultrasound sensors and the corresponding class label (see Section 7). Sensor readings are sampled at a rate of 9 samples per second. The second one contains four sensor readings named 'simplified distances' and the corresponding class label (see Section 7). These simplified distances are referred to as the 'front distance', 'left distance', 'right distance' and 'back distance'. They consist, respectively, of the minimum sensor readings among those within 60 degree arcs located at the front, left, right and back parts of the robot. The third one contains only the front and left simplified distances and the corresponding class label (see Section 7). -- 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 succesfully. 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. -- Files with different number of sensor readings were built in order to evaluate the performance of the classifiers with respect to the number of inputs. 5. Number of Instances: 5456 6. Number of Attributes -- sensor_readings_24.data: 24 numeric attributes and the class. -- sensor_readings_4.data: 4 numeric attributes and the class. -- sensor_readings_2.data: 2 numeric attributes and the class. 7. 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 -- 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 -- File sensor_readings_2.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. Class: -- Move-Forward -- Slight-Right-Turn -- Sharp-Right-Turn -- Slight-Left-Turn -- Summary Statistics: -- File sensor_readings_24.data: Max Min Mean SD US1 5.0000 0.40000 1.47162 0.80280 US2 5.0250 0.43700 2.32704 1.41015 US3 5.0290 0.47000 2.48935 1.24743 US4 5.0170 0.83300 2.79650 1.30937 US5 5.0000 1.12000 2.95855 1.33922 US6 5.0050 1.11400 2.89307 1.28258 US7 5.0080 1.12200 3.35111 1.41369 US8 5.0870 0.85900 2.54040 1.11155 US9 5.0000 0.83600 3.12562 1.35697 US10 5.0220 0.81000 2.83239 1.30784 US11 5.0190 0.78300 2.54940 1.38203 US12 5.0000 0.77800 2.07778 1.24930 US13 5.0030 0.77000 2.12578 1.40717 US14 5.0000 0.75600 2.19049 1.57687 US15 5.0000 0.49500 2.20577 1.71543 US16 5.0000 0.42400 1.20211 1.09857 US17 5.0000 0.37300 0.98983 0.94207 US18 5.0000 0.35400 0.91027 0.88953 US19 5.0000 0.34000 1.05811 1.14463 US20 5.0000 0.35500 1.07632 1.14150 US21 5.0000 0.38000 1.01592 0.88744 US22 5.0000 0.37000 1.77803 1.57169 US23 5.0000 0.36700 1.55505 1.29145 US24 5.0000 0.37700 1.57851 1.15048 -- File sensor_readings_4.data: Max Min Mean SD SD_front 5 0.49500 1.29031 0.62670 SD_left 5 0.34000 0.68127 0.34259 SD_right 5 0.83600 1.88182 0.56253 SD_back 5 0.36700 1.27369 0.82175 -- File sensor_readings_2.data: Max Min Mean SD SD_front 5 0.49500 1.29031 0.62670 SD_left 5 0.34000 0.68127 0.34259 8. Missing Attribute Values: none 9. Class Distribution: -- Move-Forward: 2205 samples (40.41%). -- Slight-Right-Turn: 826 samples (15.13%). -- Sharp-Right-Turn: 2097 samples (38.43%). -- Slight-Left-Turn: 328 samples (6.01%).