Methods of managing an automated mobile system
DOI:
https://doi.org/10.30837/2522-9818.2024.4.067Keywords:
automated system; management; bots; performance; reliability; genetic algorithms; swarm algorithms; centralized management; decentralized management; distributed management.Abstract
Subject matter: Description of existing management methods for an automated mobile system, particularly a swarm of bots. Goal: To conduct a comparative analysis of centralized, decentralized, and distributed management methods to identify their advantages and disadvantages. Tasks: To study various approaches to managing a swarm of bots, analyze their efficiency, reliability, and applicability in different fields. Methods: Theoretical analysis of existing approaches, modeling, and simulation to assess their performance and reliability. The research includes the use of genetic algorithms, swarm algorithms, and artificial potential fields for managing bot trajectories. Results: The conducted analysis shows that centralized methods provide high accuracy and coordination but have limited fault tolerance and scalability. Decentralized methods offer greater flexibility and robustness but may have coordination issues between nodes. Distributed methods provide high autonomy and adaptability but require a reliable communication infrastructure. Genetic algorithms and swarm algorithms are effective for planning bot trajectories but have high computational costs. Conclusions: The use of different management methods depends on the specific requirements and conditions of the task. Centralized methods are suitable for tasks where precision and coordination are important, decentralized methods for tasks requiring flexibility and robustness, and distributed methods for tasks needing autonomy and adaptability. Future research should focus on integrating different approaches to enhance the efficiency and reliability of bot swarm management systems.
References
Список літератури
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Hellaoui H., Chelli A., Bagaa M., Taleb T. UAV Communication Strategies in the Next Generation of Mobile Networks. In Proceedings of the 2020 International Wireless Communications and Mobile Computing (IWCMC), Limassol, Cyprus, 15–19 June 2020; Р. 1642–1647. DOI: https://doi.org/10.1109/IWCMC48107.2020.9148312
Yang T., Shen X.S. Mission-Critical Search and Rescue Networking Based on Multi-Agent Cooperative Communication. Springer Singapore Pte. Limited, Singapore, 2020. Р. 55–76. DOI: https://doi.org/10.1007/978-981-15-4412-5_5
References
Fu, Z., Mao, Y., He, D., Yu, J., Xie, G. (2019), "Secure Multi-UAV Collaborative Task Allocation". IEEE Access, 7, Р. 35579–35587. DOI: https://doi.org/10.1109/ACCESS.2019.2902221
Yavuz, H.S., Goktas, H., Cevikalp, H., Saribas, H. (2020), "Optimal Task Allocation for Multiple UAVs". In Proceedings of the 2020 28th Signal Processing and Communications Applications Conference (SIU), Gaziantep, Turkey, 5–7 October 2020; Р. 1–4. DOI: https://doi.org/10.1109/SIU49456.2020.9302360
Roberge, V., Tarbouchi, M., Labonté, G. (2013), "Comparison of Parallel Genetic Algorithm and Particle Swarm Optimization for Real-Time UAV Path Planning". IEEE Transactions on Industrial Informatics, 9(1), Р. 132–141. DOI: https://doi.org/10.1109/TII.2012.2198665
Bai, W., Wu, X., Xie, Y., Wang, Y., Zhao, H., Chen, K., Li, Y., Hao, Y. (2018), "A Cooperative Route Planning Method for Multi-UAVs Based on the Fusion of Artificial Potential Field and B-spline Interpolation". In Proceedings of the 2018 37th Chinese Control Conference (CCC), Wuhan, China, 25–27 July 2018; Р. 6733–6738. DOI: https://doi.org/10.23919/ChiCC.2018.8483665
Wang, W., Lv, M., Ru, L., Lu, B., Hu, S., & Chang, X. (2022), "Multi-UAV Unbalanced Targets Coordinated Dynamic Task Allocation in Phases". Aerospace, 9(9), 491 р. DOI: https://doi.org/10.3390/aerospace9090491
Wang, X., Bai, Y., Sun, Z., et al. (2024), "Deep Reinforcement Learning-Based Air Combat Maneuver Decision-Making: Literature Review, Implementation Tutorial, and Future Direction". Artificial Intelligence Review, 57(1), Р. 1–25. DOI: https://doi.org/10.1007/s10462-023-10620-2
Yavuz, H.S., Goktas, H., Cevikalp, H., & Saribas, H. (2020), "Optimal Task Allocation for Multiple UAVs". In Proceedings of the 2020 28th Signal Processing and Communications Applications Conference (SIU), Gaziantep, Turkey, 5–7 October 2020; Р. 1–4. DOI: https://doi.org/10.1109/SIU49456.2020.9302360
Roberge, V., Tarbouchi, M., & Labonté, G. (2013), "Comparison of Parallel Genetic Algorithm and Particle Swarm Optimization for Real-Time UAV Path Planning". IEEE Transactions on Industrial Informatics, 9(1), Р. 132–141. DOI: https://doi.org/10.1109/TII.2012.2198665
Jia, G.W., & Wang, J.F. (2021), "Research Review of UAV Swarm Mission Planning Method". Systems Engineering and Electronics, 43(1), Р. 99–111.
Lizzio, F.F., Capello, E., Guglieri, G. (2022), "A Review of Consensus-Based Multi-Agent UAV Implementations". Journal of Intelligent & Robotic Systems, 106(2), 43 р. DOI: https://doi.org/10.1007/s10846-022-01391-5
Ammoniaci, M., Kartsiotis, S., Perria, R., Storchi, P. (2021), "State of the Art of Monitoring Technologies and Data Processing for Precision Viticulture". Agriculture, 11(3), 201 р. DOI: https://doi.org/10.3390/agriculture11030201
Yang, T., Shen, X.S. (2020), "Mission-Critical Search and Rescue Networking Based on Multi-Agent Cooperative Communication". Springer Singapore Pte. Limited: Singapore; Р. 55–76. DOI: https://doi.org/10.1007/978-981-15-4412-5_5
Koushik, A.M., Hu, F., & Kumar, S. (2019), "Deep-Learning-Based Node Positioning for Throughput-Optimal Communications in Dynamic UAV Swarm Network". IEEE Transactions on Cognitive Communications and Networking, 5(3), Р. 554–566. DOI: https://doi.org/10.1109/TCCN.2019.2907520
Hellaoui, H., Chelli, A., Bagaa, M., Taleb, T. (2020), "UAV Communication Strategies in the Next Generation of Mobile Networks". In Proceedings of the 2020 International Wireless Communications and Mobile Computing (IWCMC), Limassol, Cyprus, 15–19 June 2020; Р. 1642–1647. DOI: https://doi.org/10.1109/IWCMC48107.2020.9148312
Walter, V., Staub, N., Franchi, A., Saska, M. (2019), "UVDAR System for Visual Relative Localization with Application to Leader–Follower Formations of Multirotor UAVs". IEEE Robotics and Automation Letters, 4(3), Р. 2637–2644. DOI: https://doi.org/10.1109/LRA.2019.2901683
Bala, J.A., Adeshina, S.A., Aibinu, A.M. (2022), "Advances in Visual Simultaneous Localization and Mapping Techniques for Autonomous Vehicles: A Review". Sensors, 22(22), 8943 р. DOI: https://doi.org/10.3390/s22228943
Causa, F., Vetrella, A.R., Fasano, G., Accardo, D. (2018), "Multi-UAV Formation Geometries for Cooperative Navigation in GNSS-Challenging Environments". In Proceedings of the 2018 IEEE/ION Position, Location and Navigation Symposium (PLANS), Monterey, CA, USA, 23–26 April 2018; Р. 775–785. DOI: https://doi.org/10.1109/PLANS.2018.8373434
Kaleem, Z., Ahmad, I., & Duong, T.Q. (2022), "UAVs Path Planning by Particle Swarm Optimization Based on Visual-SLAM Algorithm". Springer: Singapore. DOI: https://doi.org/10.1007/978-981-19-12924-7
Nguyen, T.H., Nguyen, T.M., & Xie, L. (2021), "Flexible and Resource-Efficient Multi-Robot Collaborative Visual-Inertial-Range Localization". IEEE Robotics and Automation Letters, 7(2), Р. 928–935. DOI: https://doi.org/10.1109/LRA.2021.3136286
Bala, J.A., Adeshina, S.A., Aibinu, A.M. (2022), "A Modified Visual Simultaneous Localization and Mapping (V-SLAM) Technique for Road Scene Modelling". In Proceedings of the 2022 IEEE Nigeria 4th International Conference on Disruptive Technologies for Sustainable Development (NIGERCON), Lagos, Nigeria, 5–7 April 2022; pp. 1–5. DOI: https://doi.org/10.1109/NIGERCON54645.2022.9803124
Wang, D., Lian, B., Liu, Y., &Gao, B. (2022), "A Cooperative UAV Swarm Localization Algorithm Based on Probabilistic Data Association for Visual Measurement". IEEE Sensors Journal, 22(22), Р. 19635–19644. DOI: https://doi.org/10.1109/JSEN.2022.3213324
Hellaoui, H., Chelli, A., Bagaa, M., & Taleb, T. (2020), "UAV Communication Strategies in the Next Generation of Mobile Networks". In Proceedings of the 2020 International Wireless Communications and Mobile Computing (IWCMC), Limassol, Cyprus, 15–19 June 2020; Р. 1642–1647. DOI: https://doi.org/10.1109/IWCMC48107.2020.9148312
Yang, T., Shen, X.S. (2020), "Mission-Critical Search and Rescue Networking Based on Multi-Agent Cooperative Communication". Springer Singapore Pte. Limited, Singapore, Р. 55–76. DOI: https://doi.org/10.1007/978-981-15-4412-5_5
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