Neural Network Based Control System for Robots Group Operating in 2-d Uncertain Environment
https://doi.org/10.17587/mau.21.470-479
Abstract
This study is devoted to development of a neural network based control system of robots group. The control system performs estimation of an environment state, searching the optimal path planning method, path planning, and changing the trajectories on via the robots interaction. The deep learning neural networks implements the optimal path planning method, and path planning of the robots. The first neural network classifies the environment into two types. For the first type a method of the shortest path planning is used. For the second type a method of the most safety path planning is used. Estimation of the path planning algorithm is based on the multi-objective criteria. The criterion includes the time of movement to the target point, path length, and minimal distance from the robot to obstacles. A new hybrid learning algorithm of the neural network is proposed. The algorithm includes elements of both a supervised learning as well as an unsupervised learning. The second neural network plans the shortest path. The third neural network plans the most safety path. To train the second and third networks a supervised algorithm is developed. The second and third networks do not plan a whole path of the robot. The outputs of these neural networks are the direction of the robot’s movement in the step k. Thus the recalculation of the whole path of the robot is not performed every step in a dynamical environment. Likewise in this paper algorithm of the robots formation for unmapped obstructed environment is developed. The results of simulation and experiments are presented.
Supporting agencies: Research is supported by Russian Science Foundation (grant 16-19-00001), and was executed in Southern Federal University.
About the Authors
A. R. Gaiduk
Southern Federal University
Russian Federation
Russian Federation
D.Sc
Shevchenko str. 2, Taganrog
O. V. Martjanov
Southern Federal University
Russian Federation
Russian Federation
C.Sc
Shevchenko str. 2, Taganrog
M. Yu. Medvedev
Southern Federal University
Russian Federation
Russian Federation
Medvedev Mikhail Yu., D.Sc
Shevchenko str. 2, Taganrog
V. Kh. Pshikhopov
Southern Federal University
Russian Federation
Russian Federation
D.Sc
Shevchenko str. 2, Taganrog
N. Hamdan
Southern Federal University
Russian Federation
Russian Federation
Postgraduate student
Shevchenko str. 2, Taganrog
A. Farhood
Southern Federal University
Russian Federation
Russian Federation
Postgraduate student
Shevchenko str. 2, Taganrog
References
1. Pshikhopov V., Medvedev M. Position-Path Control of a Vehicle, Path Planning for Vehicles Operating in Uncertain 2D Environments, Elsevier, Butterworth-Heinemann, 2017, pp. 1—23.
2. Yang L., Qi J., Song D., Han J., Xia Y. Survey of Robot 3D Path Planning Algorithms, Journal of Control Science and Engineering, 2016, vol. 2016, 22 p.
3. La Valle S. Planning Algorithms. Cambridge University Press, 2006, 842 p.
4. Mac T. T., Copot C., Tran D. T., De Keyser R. Heuristic approaches in robot path planning: A survey, Robotics and Autonomous Systems, 2016, vol. 86, pp. 13—28.
5. 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 (3), pp. 463—497.
6. Bayindir L. A review of swarm robotics tasks, Neurocomputing, 2016, available at: https://doi.org/10.1016/j.neucom.2015.05.116.
7. Shi Y. Particle swarm optimization: Developments, applications and resources, Evolutionary Computing, 2001 IEEE International Conference on, 2001, vol. 1, pp. 81—86.
8. Uny Cao Y., Fukunaga A. S., Kahng A. B. Cooperative Mobile Robotics: Antecedents and Directions, Autonomous Robots, 1997, vol. 4, pp. 1—23.
9. Shlyakhov N. E., Vatamaniuk I. V., Ronzhin A. L. Review of the Methods and Algorithms of a Robot Swarm Aggregation, Mekhatronika, Avtomatizatsiya, Upravlenie, 2017, vol. 18(1), pp. 22—29 (in Russian).
10. Yu X.- Y., Fan Z.- Y., Ou L.-L., Zhu F., Guo Y.-K. Optimal Path Planning Satisfying Complex Task Requirement in Uncertain Environment, Robotica, 2019, vol. 37 (11), pp. 1956—1970.
11. Patle B. K., Pandey A., Jagadeesh A., Parhi D. R. Path planning in uncertain environment by using firefly algorithm, Defence Technology, 2018, vol. 14(6), pp. 691—701.
12. Kamil F., Hong T. S., Khaksar W., Moghrabiah M. Ya., Zulkifli N., Ahmad S. A. New robot navigation algorithm for arbitrary unknown dynamic environments based on future prediction and priority behavior, Expert Systems with Applications, 2017, vol. 86, pp. 274—291.
13. Nazarahari M., Khanmirza E., Doostie S. Multi-objective multi-robot path planning in continuous environment using an enhanced genetic algorithm, Expert Systems with Applications, 2019, vol. 115, pp. 106—120.
14. Qu Y., Zhang Y., Zhang Y. A Global Path Planning Algorithm for Fixed-wing UAVs, Journal of Intelligent and Robotic Systems: Theory and Applications, 2018, vol. 91(3—4), pp. 691—707.
15. Belogalzov D., Finaev V., Shapovalov I., Soloviev V., Medvedev M. Decentralized control of a group of robots using fuzzy logic, Journal of Engineering and Applied Sciences, 2017, vol. 12 (9), pp. 2492—2498.
16. Pshikhopov V., Medvedev M. Decentralized control of the mobile robots group in an obstructed 2-D environment, Mekhatronika, Avtomatizatsiya, Upravlenie. vol. 17(5), pp. 346—353 (in Russian).
17. Pshikhopov V., Medvedev M. Mobile Objects Control in Three-Dimensional Area Using the Hybrid Decentralized Algorithm, 5th International Conference on Control, Decision and Information Technologies, CoDIT, 2018, pp. 452—457.
18. Pshikhopov V., Medvedev M. Group control of autonomous robots motion in uncertain environment via unstable modes, SPIIRAS Proceedings, 2018, vol. 60(5), pp. 39—63 (in Russian).
19. Duan H., Qiao P. Pigeon-inspired optimization: A new swarm intelligence optimizer for air robot path planning, International Journal of Intelligent Computing and Cybernetics, 2014, vol. 7 (1), pp. 24—37.
20. Radmanesha M., Kumar M., Sarim M. Grey wolf optimization based sense and avoid algorithm in a Bayesian framework for multiple UAV path planning in an uncertain environment, Aerospace Science and Technology, 2018, vol. 77, pp. 168—179.
21. Fan X., Luo X., Yi S., Yang S., Zhang H. Optimal path planning for mobile robots based on intensified ant colony optimization algorithm, International Conference on Robotics, Intelligent Systems and Signal, Changsha, China (2003), pp. 131—136.
22. Zhang Y., Gong D., Zhang J. H. Robot path planning in certain environment using multi objective particle swarm optimization, Neurocomputing, 2013, vol. 103, 172—85.
23. Liang H. H., Lee C. Efficient collision-free path planning of multiple mobile robots system using efficient artificial bee colony algorithm, Advanced Engineering Software, 2015, vol. 79, 47—56.
24. Xu B., Yang Z., Ge Yu., Peng Z. Coalition Formation in Multi-agent Systems Based on Improved Particle Swarm Optimization Algorithm, International Journal of Hybrid Information Technology, 2015, vol. 8(3), pp. 1—8.
25. Munasypov R. A., Muslimov T. Z. UAVs Group Control Based on the Relative State Space Method, Mekhatronika, Avtomatizatsiya, Upravlenie, 2018, vol. 19, no. 2, pp. 120—125 (in Russian).
26. Wang X., Yang G.-H. Adaptive reliable coordination control for linear agent networks with intermittent communication constraints, E-LETTER on Systems, Control, and Signal Processing, 2018, vol. 362.
27. Hong-Wei G., Wen-Li D., Feng Q., Lu W. A Dissimilation Particle Swarm Optimization-Based Elman Network and Applications for Identifying and Controlling Ultrasonic Motors, Advances in Soft Computing, 2007, vol. 41, pp. 1345—1360.
28. Le Cun Ya., Yoshua B., Geoffrey H. Deep learning, Nature, 2015, vol. 521.7553, pp. 436—444.
29. Zhu J., Park T., Isola P., Efros A. A. Unpaired Image-toImage Translation using Cycle-Consistent Adversarial Networks, In IEEE International Conference on Computer Vision (ICCV), 2017.
30. Gholami B., Rudovic O., Pavlovic V. Probabilistic Unsupervised Domain Adaptation for Knowledge Transfer Across Visual Categories, The IEEE International Conference on Computer Vision (ICCV), 2017, pp. 3581—3590.
31. Li Z., Zhao T., Chen F., Hu Y., Su C.-Y., Fukuda T. Reinforcement Learning of Manipulation and Grasping Using Dynamical Movement Primitives for a Humanoidlike Mobile Manipulator, IEEE/ASME Transactions on Mechatronics, 2018, vol. 23(1), pp. 121—131.
32. Pshikhopov V., Medvedev M., Vasileva M. Neural network control system of motion of the robot in the environment with obstacles, Lecture Notes in Computer Science, 2019, vol. 11606, pp. 173—181.
33. Knyaz V. A., Vishnyakov B. V., Gorbatsevich V. S., Vizilter Yu. V., Vygolov O. V. Intelligent data processing technologies for unmanned aerial vehicles navigation and control, SPIIRAS Proceedings, 2016, vol. 45(2), pp. 26—44 (in Russian).
34. Karkishchenko A. N., Pshikhopov V. Kh. On finding the complexity of an environment for the operation of a mobile object on a plane, Automation and Remote Control, 2019, vol. 80(5), pp. 897—912.
35. Zoph B., Vasudevan V., Shlens J., Le Q. V. Learning Transferable Architectures for Scalable Image Recognition, Proc. of 2017 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2017, July, 22—25, Honolulu, Hawaii.
Review
For citations:
Gaiduk A.R., Martjanov O.V., Medvedev M.Yu., Pshikhopov V.Kh., Hamdan N., Farhood A. Neural Network Based Control System for Robots Group Operating in 2-d Uncertain Environment. Mekhatronika, Avtomatizatsiya, Upravlenie. 2020;21(8):470-479. https://doi.org/10.17587/mau.21.470-479
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