А comprehensive approach to managing robot group formation
DOI:
https://doi.org/10.30837/2522-9818.2024.2.017Keywords:
unmanned aerial vehicles; swarm; master – slave; interaction; simulation; visualization.Abstract
Subject matter: Research and development of methods for controlling swarms of unmanned aerial vehicles (UAVs) based on the "master – slave" model. This includes examining existing classifications and interactions between unmanned aerial vehicles in various formations such as groups, flocks, associations, and swarms, with the goal of creating an effective management system. Goal To improve the quality of interaction between unmanned aerial vehicles based on the "master – slave" model during flight missions through constant control between objects. Ensuring reliable execution of flight missions by implementing new management methods that account for different modes of interaction between devices. Tasks: Analyze the classification of existing UAVs; analyze the parameters and model of interaction of unmanned aerial vehicles in existing groups, flocks, associations, swarms; create a scenario of interaction between two UAVs based on the "master – slave" model; develop a program for visualizing the flight of unmanned aerial vehicles based on the "master – slave" model; conduct flight testing according to the proposed model on stages with various geospatial objects. Methods: Simulation method for developing a UAV flight visualization subsystem; graphical modeling method for creating an aircraft-type unmanned aerial vehicle model; methods of algorithm theory for developing a scenario of interaction between two UAVs. Utilization of specialized software tools for visualization and simulation of UAV behavior in real-time conditions. Results: Developed a classification of unmanned aerial vehicles; created a graphical model of the Mini-Flight-M aircraft; developed a scheme for the interaction of two UAVs in "teacher" or "mentor" modes; created a program for visualizing the flight of UAVs based on the "master – slave" model; conducted flight testing according to the proposed model on stages with various geospatial objects. The results confirmed the effectiveness of the developed model and demonstrated its applicability in various fields, including environmental monitoring, rescue operations, and other autonomous missions. Conclusions: The proposed approach to controlling a UAV swarm based on the "master – slave" model improves the quality of interaction between the devices and ensures reliable execution of flight missions. Further research should focus on optimizing energy consumption and ensuring reliable communication between swarm agents. It is also important to develop methods for protecting UAV swarms from cyberattacks and other threats to enhance their resilience and reliability during complex missions.
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References
Jia, G. W., Wang, J. F. (2021), "Research review of UAV swarm mission planning method". Systems Engineering and Electronics. 2021. Vol. 43. №. 1. P. 99–111. DOI: 10.3969/j.issn.1001-506X.2021.01.13
Do, H. T. (2021), "Formation control algorithms for multiple-uavs: a comprehensive survey" / Do H. T. et all. EAI Endorsed Transactions on Industrial Networks and Intelligent Systems, 8(27), Vol. 8(27):170230. DOI:10.4108/eai.10-6-2021.170230
Na, S., Niu, H., Lennox, B., Arvin, F. (2022), "Bio-Inspired Collision Avoidance in Swarm Systems via Deep Reinforcement Learning", IEEE Transactions on Vehicular Technology, Vol. 71, No. 3, Р. 2511–2526. DOI: 10.1109/TVT.2022.3145346
Pawełczyk, M. Ł., Wojtyra, M. (2020), "Real World Object Detection Dataset for Quadcopter Unmanned Aerial Vehicle Detection", IEEE Access, Vol. 8, Р. 174394–174409. DOI: 10.1109/ACCESS.2020.3026192
Zhang, J., et al. (2019), "Perdix: A Swarm of Swarming UAVs", Journal of Field Robotics, Vol. 36, No. 6, Р. 1240–1255.
Smith, J., et al. (2020), "LOCUST: Low-Cost UAV Swarm Technology for Tactical Operations," Defense Technology, Vol. 16, No. 3, Р. 205–215.
Sytsma, J., Thompson, D., and Sicoli, J. (2023), "Drone Ultrasonic Detection", Australian International Aerospace Congress. available online: https://search.informit.org/doi/abs/10.3316/informit.063769306863002 (last accessed 17 May 2024).
Kritsky, D. N., Ovsiannik, V. M., Pogudina, O. K., Shevel, V. V., and Druzhinin, E. A. (2019), "Model for intercepting targets by the unmanned aerial vehicle", Advances in Intelligent Systems and Computing. Р. 197–206. DOI: https://doi.org/10.1007/978-3-030-25741-5_20
Pohudina, O., Kritskiy, D., Koba, S., and Pohudin, A. (2020), "Assessing unmanned traffic bandwidth", Integrated Computer Technologies in Mechanical Engineering: Synergetic Engineering. Р. 447–458. DOI: https://doi.org/10.1007/978-3-030-37618-5_38
Petersen, K. (2021), "Tackling air pollution with autonomous drones", MIT School of Engineering. available online: https://news.mit.edu/2021/tackling-air-pollution-with-autonomous-drones-0624 (last accessed 17 May 2024).
Chu, J. (2023), "New traffic cop algorithm helps a drone swarm stay on task", MIT News Office. available online: https://news.mit.edu/2023/new-traffic-cop-algorithm-drone-swarm-wireless-0313 (last accessed 17 May 2024).
Lizzio, F. F., Capello, E., and Guglieri, G. (2022), "A Review of Consensus-based Multi-agent UAV Implementations", Journal of Intelligent & Robotic Systems, Vol. 106, (43). 1719 р. DOI: https://doi.org/10.1007/s10846-022-01743-9
Padmaja, B., Moorthy, C.H.V.K.N.S.N., Venkateswarulu, N., and Bala, M.M. (2023), "Exploration of issues, challenges and latest developments in autonomous cars", Journal of Big Data, Vol. 10(1). Р. 1–24. DOI: https://doi.org/10.1186/s40537-023-00701-y
Enwerem, C. and Baras, J.S. (2023), "Consensus-Based Leader-Follower Formation Tracking for Control-Affine Nonlinear Multiagent Systems", Electrical Engineering and Systems Science. DOI: https://doi.org/10.48550/arXiv.2309.09156
Xu, Z., Yan, T., Yang, S.X., and Gadsden, S.A. (2023), "Distributed Leader Follower Formation Control of Mobile Robots based on Bioinspired Neural Dynamics and Adaptive Sliding Innovation Filter", IEEE Transactions on Industrial Informatics. DOI: https://doi.org/10.1109/TII.2023.3272666
Ye, Y., Hu, S., Zhu, X., and Sun, Z. (2024), "An Improved Super-Twisting Sliding Mode Composite Control for Quadcopter UAV Formation", Machines, 12(1), 32. DOI: https://doi.org/10.3390/machines1201003
Sarhadi, P., et al. (2023), "Adaptive formation motion planning and control of autonomous underwater vehicles using deep reinforcement learning", IEEE Journal of Oceanic Engineering. Р. 1–33. available online: https://arxiv.org/ftp/arxiv/papers/2304/2304.00225.pdf (last accessed 17 May 2024).
Kritskiy, D., Yashin, S., and Koba, S. (2021), "Unmanned aerial vehicle mass model peculiarities". Р. 299–308. DOI: https://doi.org/10.1007/978-3-030-58124-4_29
"Distributed Leader Follower Formation Control of Mobile Robots based on Bioinspired Neural Dynamics and Adaptive Sliding Innovation Filter" (2023), available online: https://arxiv.org/pdf/2301.01234.pdf (last accessed 17 May 2024).
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