|Article title||ALGORITHMS OF MOTION PLANNING OF UNDERWATER VEHICLES|
|Authors||O.B. Lebedev, Е.М. Lebedeva, V.A. Pestov|
|Section||SECTION I. MODELING AND DESIGN|
|Month, Year||04, 2015 @en|
|Abstract||The paper presents the wave algorithms used in the planning of underwater vehicle movement in the space containing obstacles. It is recognized that the safest and most effective way to study the underwater environment is the use of technology, providing indirect presence of man in the water. An important role to play underwater vehicles developed control system. Within a short period, they have demonstrated their effectiveness in carrying out fairly complex deepwater surveillance and search and work-finding and discovered a number of important new applications for offshore geological exploration, explore the underwater environment and ecological monitoring the aquatic environment. Modern multipurpose underwater vehicles represent a new class of underwater robotic objects with their inherent problems and practical application of the technology, the composition of systems and functional properties. As the main scheduling algorithm A * algorithm is used with additional heuristics, which lets you find the optimum and safe way from the initial to the target position based on the detected obstacles in the space, which is an underwater environment condition. Scheduling may be carried out in two-dimensional and three-dimensional spaces. Experiments were carried out in the way of planning and three-dimensional spaces and obtained results confirming the efficiency of the considered algorithms.|
|Keywords||Wave algorithms; planning movement; submersible; space; underwater environment; facilities; providing indirect presence of a person under the water management system; marine geological exploration; explore the underwater environment and ecological monitoring the aquatic environment; the robot object technology; two-dimensional and a three-dimensional space.|
|References||1. Ageev M.D., Kiselev L.V., Matvienko Yu.V. i dr. Avtonomnye podvodnye roboty: sistemy i tekhnologii [Autonomous underwater robots: systems and technologies]. Moscow: Nauka, 2005, 398 p.
2. Algoritm poiska A* [The A*search algorithm]. Available at: http://ru.wikipedia.org/wiki/The A*search algorithm.
3. Russell S., Norvig P. Artificial Intelligence: A Modern Approach (3rd ed.). Prentice Hall, 2010, pp. 971-1011.
4. Lau B., Sprunk C., Burgard W. Improved Updating of Euclidean Distance Maps and Voronoi Diagrams, IEEE Intl. Conf. on Intelligent Robots and Systems (IROS). Taipei, Taiwan, 2010.
5. Kalra N., Ferguson D., Stentza A. Incremental reconstruction of generalized Voronoi diagrams on grids, Robotics and Autonomous Systems, 2009, No. 57, pp. 123-128.
6. Guzik V.Ph., Chernukhin Yu.V., Pyavchenko A.O., Polenov M.Yu., Pereverzev V.A. and Saprykin R.V. Neural network method of intellectual planning of mobile robotic object movement in the conditions of uncertainty. Advances in Robotics, Mechatronics and Circuits. Proceedings of the 18th International Conference on Circuits (part of CSCC '14) and the 2014 International Conference on Mechatronics and Robotics, Structural Analysis (MEROSTA 2014). Santorini Island, Greece July 17-21, 2014, pp. 194-200.
7. Guzik V.Ph., Chernukhin Yu.V., Pyavchenko A.O., Pereverzev V.A. and Saprykin R.V. Principles of structural organization of the intellectual movement planning system for mobile robotic object. Advances in Robotics, Mechatronics and Circuits. Proceedings of the 18th International Conference on Circuits (part of CSCC '14) and the 2014 International Conference on Mechatronics and
Robotics, Structural Analysis (MEROSTA 2014). Santorini Island, Greece July 17-21, 2014, pp. 223-227.
8. Ioan A. Șucan, Mark Moll, Lydia E. Kavraki. The Open Motion Planning Library, IEEE Robotics & Automation Magazine, December 2012, No. 19 (4), pp. 72-82. Available at: http://ompl.kavrakilab.org.
9. Pozna C., Precup R.-E., Koczy L.T., Ballagi A. Potential field-based approach for obstacle avoidance trajectories, The IPSI BgD Transactions on Internet Research, 2002, Vol. 8, No. 2, pp. 40-45.
10. Ferrara A., Rubagotti M. Sliding Mode Control of a Mobile Robot for Dynamic obstacle Avoidance Based on a Time-Varying Harmonic Potential Field, ICRA 2007 Workshop: Planning, Perception and Navigation for Intelli-gent Vehicles.
11. Padilla Castaneda M.A., Savage J., Hernandez A. and Arambula Cosнo F. Local Autonomous Robot Navigation Using Potential Fields, Motion Planning, Xing-Jian Jing (Ed.), ISBN: 978-953-7619-01-5, InTech, 2008. http://www.intechopen.com/books/motion_planning/ lo-
12. Ge S.S., Cui Y.J. New Potential Functions for Mobile Robot Path Planning, IEEE Transactions on Robotics and Automation, 2000, Vol. 16, No. 5, pp. 615-620.
13. Koren Y., Borenstein J. Potential Field Methods and Their Inherent Limitations for Mobile Robot Navigation, In Proc. The IEEE Conference on Robotics and Automation, 1991, pp. 1398-1404.
14. Shimoda S., Kuroda Y. and Iagnemma K. High Speed Navigation of Unmanned Ground Vehicles on Uneven Terrain Using Potential Fields, Robotica, July 2007, Vol. 25, No. 4, pp. 409-424.
15. Li F., Tan Y., Wang Y., Ge G. Mobile Robots Path Planning Based on Evolutionary Artificial Potential Fields Approach, In Proc. The 2nd Inter-national Conference on Computer Science and Electronics Engineering, 2013, pp. 1314-1317.
16. Tang L., Dian S., Gu G., Zhou K., Wang S., Feng X. A Novel Po-tential Field Method for Obstacle Avoidance and Path Planning of Mobile Ro-bot, In Proc. 3rd International Conference on Computer Science and Infor-mation Technology (ICCSIT), 2010, Vol. 9, pp. 633-637.
17. Macek K., Petrovic I., Ivanjko E. An Approach to Motion Planning of Indoor Mobile Robots, In Proc. IEEE International Conference on Industrial Technology, 2003, pp. 969-973.
18. Stentz A. The Focussed D* Algorithm for Real-Time Replanning, In Proc. International Joint Conference on Artificial Intelligence, 1995.