АНАЛИЗ НА МЕТОДИ ЗА АВТОНОМНО ОПРЕДЕЛЯНЕ НА РОБОТНИ ТРАЕКТОРИИ

Анатолий Каргин; Олександър Иванюк; Галина Чернева

Научен доклад ID 2057 : 2020/2

АНАЛИЗ НА МЕТОДИ ЗА АВТОНОМНО ОПРЕДЕЛЯНЕ НА РОБОТНИ ТРАЕКТОРИИ

Анатолий Каргин, Олександър Иванюк, Галина Чернева

Алгоритмите за определяне на траекторията на мобилни роботи се анализират от гледна точка на възможността за тяхното използване за автономни системи. Особено внимание се отделя на анализа на възможностите на класическите подходи за осъществяване на автономно препланиране на траектории на базата на сензорни данни при неопределеност и размитост на информационните условия. Сред алгоритмите на класическия подход най-адаптивни се оказват тези, които използват изкуствени потенциални полета и метода на Монте Карло.

В представената работа се доказва, че повечето алгоритми, включително тези, базирани на подхода на изчислителната интелигентност, осигуряват препланиране на траекторията само на базата на постоянно актуализиране на глобалната информация за околната среда. Показано е, че хибридният подход за решаване на проблема с планирането на трасето е най-адаптивният, съчетаващ техниките на глобалното и местното планиране. Методите на този подход съчетават класически модели за планиране с модели, базирани на изчислителна интелигентност. В допълнение към изложеното е показано, че въпросът за хомогенността на интегрирането на решения за различни навигационни задачи и взаимното влияние на грешките, възникващи на всеки от етапите, е все още недостатъчно изследван.

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автономен мобилен робот навигация определяне на траекторииautonomous mobile robot navigation path planning Анатолий Каргин Олександър Иванюк Галина Чернева

BIBLIOGRAPHY

[1] . Zhao, Y., Zheng, Z., Liu, Y. Survey on computational-intelligence-based UAV path planning. Knowledge-Based Systems. 2018. Vol. 158. P. 54–64.

[2] . Hoy, M., Matveev, A. S., Savkin, A. V. Algorithms for collision-free navigation of mobile robots in complex cluttered environments: A survey. Robotica. 2015. Vol. 33, No. 3. P. 463–497.

[3] . Patle, B. K., Babu L, G., Pandey, A., ta іn. A review: On path planning strategies for navigation of mobile robot. China Ordnance Society, 2019. 582–606 p.
( [3] . Patle, B. K., Babu L, G., Pandey, A., та ін. A review: On path planning strategies for navigation of mobile robot. China Ordnance Society, 2019. 582–606 p. )

[4] . Kargin, A., Panchenko, A., Ivaniuk, O., ta іn. Motion Control of Smart Autonomous Mobile System Based on the Perception Model: ICTE in Transportation and Logistics 2019. ICTE ToL 2019. Lecture Notes in Intelligent Transportation and Infrastructure. Edited Ginters E., Ruiz Estrada M., Piera Eroles M. Springer, Cham, 2020.
( [4] . Kargin, A., Panchenko, A., Ivaniuk, O., та ін. Motion Control of Smart Autonomous Mobile System Based on the Perception Model: ICTE in Transportation and Logistics 2019. ICTE ToL 2019. Lecture Notes in Intelligent Transportation and Infrastructure. Edited Ginters E., Ruiz Estrada M., Piera Eroles M. Springer, Cham, 2020. )

[5] . Kargin, A., Petrenko, T. Spatio-temporal data interpretation based on perceptional model: Advances in Spatio-Temporal Segmentation of Visual Data. Studies in Computational Intelligence. Edited Mashtalir V., Ruban I., Levashenko V. Springer, Cham, 2020.

[6] . Siegwart, R., Nourbakhsh, I. Introduction to Autonomous Mobile Robots, Second Edition: MIT Press, 2004. 321c.

[7] . Ozen, O., Sariyildiz, E., Yu, H., ta іn. Practical PID controller tuning for motion control: Proceedings – 2015 IEEE International Conference on Mechatronics, ICM 2015, IEEE Inc., 09.April.15. P. 240–245.
( [7] . Ozen, O., Sariyildiz, E., Yu, H., та ін. Practical PID controller tuning for motion control: Proceedings – 2015 IEEE International Conference on Mechatronics, ICM 2015, IEEE Inc., 09.April.15. P. 240–245. )

[8] . Ahmad, S., H., N., O., M. Modular Fuzzy Logic Controller for Motion Control of Two-Wheeled Wheelchair: Fuzzy Logic – Controls, Concepts, Theories and Applications. InTech, 2012.

[9] . Falsafi, M. H., Alipour, K., Tarvirdizadeh, B. Fuzzy motion control for wheeled mobile robots in real-time. Journal of Computational and Applied Research in Mechanical Engineering. 2019. Vol. 8, No. 2. P. 133–144.

[10] . Vinogradov, A., Terentev, A., Kochetkov, M., ta іn. Model of fuzzy regulator of mobile robot motion control system: Proceedings of the ElConRus 2019, IEEE Inc., 28.February.19. P. 2109–2112.
( [10] . Vinogradov, A., Terentev, A., Kochetkov, M., та ін. Model of fuzzy regulator of mobile robot motion control system: Proceedings of the ElConRus 2019, IEEE Inc., 28.February.19. P. 2109–2112. )

[11] . Wang, G., Liu, X., Zhao, Y., ta іn. Neural Network-Based Adaptive Motion Control for a Mobile Robot with Unknown Longitudinal Slipping. Chinese Journal of Mechanical Engineering (English Edition). 2019. Vol. 32, No. 1. P. 61.
( [11] . Wang, G., Liu, X., Zhao, Y., та ін. Neural Network-Based Adaptive Motion Control for a Mobile Robot with Unknown Longitudinal Slipping. Chinese Journal of Mechanical Engineering (English Edition). 2019. Vol. 32, No. 1. P. 61. )

[12] . Wang, C., Liu, X., Yang, X., ta іn. Trajectory tracking of an omni-directional wheeled mobile robot using a model predictive control strategy. Applied Sciences (Switzerland). 2018. Vol. 8, No. 2.
( [12] . Wang, C., Liu, X., Yang, X., та ін. Trajectory tracking of an omni-directional wheeled mobile robot using a model predictive control strategy. Applied Sciences (Switzerland). 2018. Vol. 8, No. 2. )

[13] . Choset, H. Principles of robot motion: theory, algorithms, and implementation: Cambridge Mass.: MIT Press, 2005.

[14] . Russell, S., Norvig, P. Artificial Intelligence: a Modern Approach: 2010.

[15] . Goerzen, C., Kong, Z., Mettler, B. A survey of motion planning algorithms from the perspective of autonomous UAV guidance. Journal of Intelligent and Robotic Systems: Theory and Applications. 2010. Vol. 57, No. 1–4. P. 65–100.

[16] . Alt, H., Welzl, E. Visibility graphs and obstacle-avoiding shortest paths. Zeitschrift für Operations Research. 1988. Vol. 32. P. 145–164.

[17] . Takahashi, O., Schilling, R. J. Motion Planning in a Plane Using Generalized Voronoi Diagrams. IEEE Transactions on Robotics and Automation. 1989. Vol. 5, No. 2. P. 143–150.

[18] . LaValle, S. M. Planning algorithms: Cambridge University Press, 2006. 826c.

[19] . Wein, R., Berg, J. P. Van Den, Halperin, D. The visibility-Voronoi complex and its applications: Computational Geometry: Theory and Applications, January.07. P. 66–87.

[20] . Niu, H., Savvaris, A., Tsourdos, A., ta іn. Voronoi-Visibility Roadmap-based Path Planning Algorithm for Unmanned Surface Vehicles. Journal of Navigation. 2019. Vol. 72, No. 4. P. 850–874.
( [20] . Niu, H., Savvaris, A., Tsourdos, A., та ін. Voronoi-Visibility Roadmap-based Path Planning Algorithm for Unmanned Surface Vehicles. Journal of Navigation. 2019. Vol. 72, No. 4. P. 850–874. )

[21] . Tzafestas, S. G. Introduction to Mobile Robot Control: Elsevier, 2014. 691c.

[22] . Latombe, J.-C., Latombe, J.-P. Exact Cell Decomposition: Robot Motion Planning. Springer US, 1991.

[23] . Choset, H., Pignon, P. Coverage Path Planning: The Boustrophedon Cellular Decomposition: Field and Service Robotics. Springer London, 1998.

[24] . Acar, E. U., Choset, H., Rizzi, A. A., ta іn. Morse decompositions for coverage tasks. International Journal of Robotics Research. 2002. Vol. 21, No. 4. P. 331–344.
( [24] . Acar, E. U., Choset, H., Rizzi, A. A., та ін. Morse decompositions for coverage tasks. International Journal of Robotics Research. 2002. Vol. 21, No. 4. P. 331–344. )

[25] . Moravec, H. P., Elfes, A. High resolution maps from wide angle sonar: Proceedings – IEEE International Conference on Robotics and Automation, IEEE Inc., 85. P. 116–121.

[26] . Galceran, E., Carreras, M. A survey on coverage path planning for robotics. Robotics and Autonomous Systems. 2013. Vol. 61, No. 12. P. 1258–1276.

[27] . Noborio, H., Naniwa, T., Arimoto, S. A quadtree‐based path‐planning algorithm for a mobile robot. Journal of Robotic Systems. 1990. Vol. 7, No. 4. P. 555–574.

[28] . Lingelbach, F. Path planning using probabilistic cell decomposition: Proceedings – IEEE International Conference on Robotics and Automation, IEEE Inc., 04. P. 467–472.

[29] . Khatib, O. Real-time obstacle avoidance for manipulators and mobile robots: Proceedings – IEEE International Conference on Robotics and Automation, IEEE Inc., 85. P. 500–505.

[30] . Sato, K. Deadlock-free motion planning using the Laplace potential field. Advanced Robotics. 1992. Vol. 7, No. 5. P. 449–461.

[31] . Vadakkepat, P., Tan, K. C., Ming-Liang, W. Evolutionary artificial potential fields and their application in real time robot path planning: Proceedings of the CEC 2000, IEEE Computer Society. P. 256–263.

[32] . Jaradat, M. A. K., Garibeh, M. H., Feilat, E. A. Autonomous mobile robot dynamic motion planning using hybrid fuzzy potential field. Soft Computing. 2012. Vol. 16, No. 1. P. 153–164.

[33] . Faverjon, B., Tournassoud, P. Probabilistic Roadmaps for Path Planning in High-Dimensional Configuration Spaces. 1996.

[34] . LaValle, S. Rapidly-exploring random trees: a new tool for path planning. 1998.

[35] . Karaman, S., Frazzoli, E. Incremental sampling-based algorithms for optimal motion planning: Robotics: Science and Systems, MIT Press Journals, 11. P. 267–274.

[36] . Naderi, K., Rajamaki, J., Hamalainen, P. RT-RRT∗: A real-time path planning algorithm based on RRT∗: Proceedings of the 8th ACM SIGGRAPH Conference on Motion in Games, MIG 2015, Association for Computing Machinery, Inc, 16.November.15. P. 113–118.

[37] . Hank, M., Haddad, M. A hybrid approach for autonomous navigation of mobile robots in partially-known environments. Robotics and Autonomous Systems. 2016. Vol. 86. P. 113–127.

[38] . Ruiz, E., Acuña, R. Safe navigation of mobile robots using a hybrid navigation framework with a fuzzy logic decision process: Communications in Computer and Information Science, Springer Verlag, 16. P. 39–56.

[39] . Pandey, A., Parhi, D. R. Autonomous mobile robot navigation in cluttered environment using hybrid Takagi-Sugeno fuzzy model and simulated annealing algorithm controller. World Journal of Engineering. 2016. Vol. 13, No. 5. P. 431–440.