1. ELARTU — Інституційний репозитарій ТНТУ імені Івана Пулюя
  2. Вісник ТНТУ
  3. 2003
  4. Вісник ТДТУ, 2003, том 8, № 2
Please use this identifier to cite or link to this item: http://elartu.tntu.edu.ua/handle/lib/33706

Title: Метод побудови локальної карти середовища мобільного робота
Other Titles: The method of local area map construction of mobile robot
Authors: Коваль, В.
Koval, V.
Affiliation: Тернопільська академія народного господарства
Bibliographic description (Ukraine): Коваль В. Метод побудови локальної карти середовища мобільного робота / Коваль В. // Вісник ТДТУ. — Т. : ТНТУ, 2003. — Том 8. — № 2. — С. 80–88. — (Приладобудування та інформаційно-вимірювальні технології).
Bibliographic description (International): Koval V. (2003) Metod pobudovy lokalnoi karty seredovyshcha mobilnoho robota [The method of local area map construction of mobile robot]. Scientific Journal of TSTU (Tern.), vol. 8, no 2, pp. 80-88 [in Ukrainian].
Is part of: Вісник Тернопільського національного технічного університету, 2 (8), 2003
Scientific Journal of the Ternopil National Technical University, 2 (8), 2003
Journal/Collection: Вісник Тернопільського національного технічного університету
Issue: 2
Volume: 8
Issue Date: 2003
Submitted date: 10-Feb-2003
Date of entry: 29-Dec-2020
Publisher: ТНТУ
TNTU
Place of the edition/event: Тернопіль
Ternopil
UDC: 681.3
Number of pages: 9
Page range: 80-88
Start page: 80
End page: 88
Abstract: В даній статті розглядаються методи представлення локальної карти середовища, отриманої від інфрачервоних і ультразвукових сенсорів. Запропоновано метод побудови локальної карти середови-ща мобільного робота, який має меншу обчислювальну складність і вищу універсальність у порівнянні з відомими методами, що є важливим при оперуванні робота в on-line режимі. Результати імітаційного моделювання показали високу точність запропонованого методу.
The construction of local area maps on the based on heterogeneous sensor readings is considered in this paper. Presented in this paper method for the construction of local area maps displays lower calculation complexity and broader universality compared to existing methods and this is important for on-line robot activity. The simulation results showed the high accuracy of the method.
URI: http://elartu.tntu.edu.ua/handle/lib/33706
ISSN: 1727-7108
Copyright owner: © Тернопільський національний технічний університет імені Івана Пулюя
References (Ukraine): 1. Popov E.P., Oismennyj G.V. The robotics bases. - M: “High school”. - 1990 – 224 p.
2. Dorst L. An Introduction to Robotics for the computer sciences, University of Amsterdam. - 1993 -78 p.
3. Durrant-Whyte H. F. Integration, Coordination and Control of Multi-sensor Robot Systems. - Kluwer, Boston, MA – 1988 – 312 p.
4. Joris van Dam. Environment Modeling for Mobile Robots: Neural Learning for Sensor Fusion, Amsterdam: University van Amsterdam – Met lit. opg. - 1998 – 225 p.
5. Barraquand J., Langlois B., Latombe J. Numerical potential field techniques for robot path planning, Report No. STAN CS 89 1285. Dept. of Computer Science, Stanford University. –1989 – 89 p.
6. Besierre P., Dedieu E., Mazer E. Representing robot/environment interactions using probabilities: the beam in the beam experiment, From Perception to Action Conference, IEEE computer society press. - Sept. – 1994 – 73 p.
7. Borenstein J., Koren Y. Real-time obstacle avoidance for fast mobile robots. IEEE Transactions on Systems, Man, and Cybernetics. - Vol. 19, No. 5. - Sept./Oct. 1989. - pp. 1179-1187
8. Mongi A. Abidi, Rafael C. Gonzalez. Data fusion in robotics and machine intelligence. Academic Press, Inc. – 1992 – 546 p.
9. Duro R., Santos J., Graña M. Biologically inspired robot behavior engineering. - Physica-Verlag. – 2003 – 438 p.
10. Poncela A., Perez E., Bandera A., Urdiales C., Sandoval F. Efficient integration of metric and topological maps for directed exploration of unknown environments. Robotics and Autonomous Systems. - No. 41 (2002). - pp. 21–39.
11. Maaref H., Barret C. Sensor-based navigation of a mobile robot in an indoor environment. Robotics and Autonomous Systems. - No. 38 (2002) – pp. 1–18.
12. Brooks R., Iyengar S. Multi-sensor fusion: fundamentals and applications with software. Prentice-Hall PTR. – 1998 – 416 p.
13. Crowly J., Demazeau Y. Principles and techniques for sensor data fusion. Signal Processing. - No. 32. - 1993 - pp. 5-27.
14. Golovko V.A. Neurointelligence: theory and application. BPI, Brest. - 1999 - 228 p.
15. Flynn A.M. Combining sonar and infrared sensors for mobile robot navigation. International journal of robotic research. Vol. 7. No. 6. – 1988 – pp. 32-37.
16. Benet G., Blanes F., Simó J., Pérez P. Using infrared sensors for distance measurement in mobile robots. Robotics and Autonomous Systems. - 2002- No. 40. - pp. 255–266.
17. Chapman S. MATLAB Programming for Engineers. 2nd Edition. Brooks/Cole Publishing Company. – 2002 – 348 p.
References (International): 1. Popov E.P., Oismennyj G.V. The robotics bases, M: "High school", 1990 – 224 p.
2. Dorst L. An Introduction to Robotics for the computer sciences, University of Amsterdam, 1993 -78 p.
3. Durrant-Whyte H. F. Integration, Coordination and Control of Multi-sensor Robot Systems, Kluwer, Boston, MA – 1988 – 312 p.
4. Joris van Dam. Environment Modeling for Mobile Robots: Neural Learning for Sensor Fusion, Amsterdam: University van Amsterdam – Met lit. opg, 1998 – 225 p.
5. Barraquand J., Langlois B., Latombe J. Numerical potential field techniques for robot path planning, Report No. STAN CS 89 1285. Dept. of Computer Science, Stanford University. –1989 – 89 p.
6. Besierre P., Dedieu E., Mazer E. Representing robot/environment interactions using probabilities: the beam in the beam experiment, From Perception to Action Conference, IEEE computer society press, Sept, 1994 – 73 p.
7. Borenstein J., Koren Y. Real-time obstacle avoidance for fast mobile robots. IEEE Transactions on Systems, Man, and Cybernetics, Vol. 19, No. 5, Sept./Oct. 1989, pp. 1179-1187
8. Mongi A. Abidi, Rafael C. Gonzalez. Data fusion in robotics and machine intelligence. Academic Press, Inc, 1992 – 546 p.
9. Duro R., Santos J., Graña M. Biologically inspired robot behavior engineering, Physica-Verlag, 2003 – 438 p.
10. Poncela A., Perez E., Bandera A., Urdiales C., Sandoval F. Efficient integration of metric and topological maps for directed exploration of unknown environments. Robotics and Autonomous Systems, No. 41 (2002), pp. 21–39.
11. Maaref H., Barret C. Sensor-based navigation of a mobile robot in an indoor environment. Robotics and Autonomous Systems, No. 38 (2002) – pp. 1–18.
12. Brooks R., Iyengar S. Multi-sensor fusion: fundamentals and applications with software. Prentice-Hall PTR, 1998 – 416 p.
13. Crowly J., Demazeau Y. Principles and techniques for sensor data fusion. Signal Processing, No. 32, 1993 - pp. 5-27.
14. Golovko V.A. Neurointelligence: theory and application. BPI, Brest, 1999 - 228 p.
15. Flynn A.M. Combining sonar and infrared sensors for mobile robot navigation. International journal of robotic research. Vol. 7. No. 6, 1988 – pp. 32-37.
16. Benet G., Blanes F., Simó J., Pérez P. Using infrared sensors for distance measurement in mobile robots. Robotics and Autonomous Systems, 2002- No. 40, pp. 255–266.
17. Chapman S. MATLAB Programming for Engineers. 2nd Edition. Brooks/Cole Publishing Company, 2002 – 348 p.
Content type: Article
Appears in Collections:Вісник ТДТУ, 2003, том 8, № 2

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