Synergistic UAV Motion: A Comprehensive Review on Advancing Multi-Agent Coordination
Review Article  ·  Published: 29 October 2024
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ICCK Transactions on Sensing, Communication, and Control
Volume 1, Issue 2, 2024: 72-88
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Synergistic UAV Motion: A Comprehensive Review on Advancing Multi-Agent Coordination

1 Interdisciplinary Research Centre for Aviation and Space Exploration, King Fahd University of Petroleum and Minerals, Dhahran 31261, Kingdom of Saudi Arabia
2 Electronic Engineering Department, Maynooth International Engineering College, Maynooth University, Maynooth, Co. Kildare, Ireland
3 Electronic Engineering Department, Usman Institute of Technology, Karachi 75300, Pakistan
* Corresponding Author: Ghulam E Mustafa Abro, [email protected]
Volume 1, Issue 2
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Abstract

Collective motion has been a pivotal area of research, especially due to its substantial importance in Unmanned Aerial Vehicle (UAV) systems for several purposes, including path planning, formation control, and trajectory tracking. UAVs significantly enhance coordination, flexibility, and operational efficiency in practical applications such as search-and-rescue operations, environmental monitoring, and smart city construction. Notwithstanding the progress in UAV technology, significant problems persist, especially in attaining dependable and effective coordination in intricate, dynamic, and unexpected settings. This study offers a comprehensive examination of the fundamental principles, models, and tactics employed to comprehend and regulate collective motion in UAV systems. This paper methodically analyses recent breakthroughs, exposes deficiencies in existing approaches, and emphasises case studies demonstrating the practical application of collective motion. The survey examines the substantial practical effects of collective motion on improving UAV operations, emphasizing scalability, resilience, and adaptability. This review is significant for its potential to inform future research and practical applications. It seeks to provide a systematic framework for the advancement of more resilient and scalable UAV collaboration models, aiming to tackle the ongoing challenges in the domain. The insights offered are essential for academics and practitioners aiming to enhance UAV collaboration in dynamic environments, facilitating the development of more sophisticated, flexible, and mission-resilient multi-UAV systems. This study is set to significantly advance UAV technology, having extensive ramifications for several industries.

Graphical Abstract

Synergistic UAV Motion: A Comprehensive Review on Advancing Multi-Agent Coordination

Keywords

collective motion dynamic agent systems formation control path planning and swarm intelligence

Data Availability Statement

Not applicable.

Funding

This work was supported by the Interdisciplinary Research Centre for Aviation and Space Exploration (IRCASE), King Fahd University of Petroleum and Minerals Kingdom of Saudi Arabia.

Conflicts of Interest

The authors declare no conflicts of interest.

Ethical Approval and Consent to Participate

Not applicable.

References

  1. Garnier, S., Gautrais, J., & Theraulaz, G. (2007). The biological principles of swarm intelligence. Swarm intelligence, 1, 3-31.
    [CrossRef] [Google Scholar]
  2. Dorigo, M., Birattari, M., & Stutzle, T. (2006). Ant colony optimization. IEEE computational intelligence magazine, 1(4), 28-39.
    [CrossRef] [Google Scholar]
  3. Alotaibi, E. T., Alqefari, S. S., & Koubaa, A. (2019). Lsar: Multi-uav collaboration for search and rescue missions. IEEE Access, 7, 55817-55832.
    [CrossRef] [Google Scholar]
  4. Ali, Z. A., Han, Z., & Masood, R. J. (2021). Collective motion and self-organization of a swarm of UAVs: A cluster-based architecture. Sensors, 21(11), 3820.
    [CrossRef] [Google Scholar]
  5. Muñoz, J., López, B., Quevedo, F., Monje, C. A., Garrido, S., & Moreno, L. E. (2021). Multi UAV coverage path planning in urban environments. Sensors, 21(21), 7365.
    [CrossRef] [Google Scholar]
  6. Vicsek, T., & Zafeiris, A. (2012). Collective motion. Physics Reports, 517(3–4), 71–140.
    [CrossRef] [Google Scholar]
  7. Yan, C., Xiang, X., & Wang, C. (2020). Towards real-time path planning through deep reinforcement learning for a UAV in dynamic environments. Journal of Intelligent & Robotic Systems, 98(2), 297-309.
    [CrossRef] [Google Scholar]
  8. Luo, Q., & Duan, H. (2017). Distributed UAV flocking control based on homing pigeon hierarchical strategies. Aerospace Science and Technology, 70, 257-264.
    [CrossRef] [Google Scholar]
  9. Garcia, G. A., & Keshmiri, S. S. (2016). Biologically inspired trajectory generation for swarming UAVs using topological distances. Aerospace Science and Technology, 54, 312-319.
    [CrossRef] [Google Scholar]
  10. Vásárhelyi, G., Virágh, C., Somorjai, G., Nepusz, T., Eiben, A. E., & Vicsek, T. (2018). Optimized flocking of autonomous drones in confined environments. Science Robotics, 3(20), eaat3536.
    [CrossRef] [Google Scholar]
  11. Olfati-Saber, R. (2006). Flocking for multi-agent dynamic systems: Algorithms and theory. IEEE Transactions on automatic control, 51(3), 401-420.
    [CrossRef] [Google Scholar]
  12. Nagy, M., Ákos, Z., Biro, D., & Vicsek, T. (2010). Hierarchical group dynamics in pigeon flocks. Nature, 464(7290), 890-893.
    [CrossRef] [Google Scholar]
  13. Duan, H., Huo, M., & Fan, Y. (2023). From animal collective behaviors to swarm robotic cooperation. National Science Review, 10(5), nwad040.
    [CrossRef] [Google Scholar]
  14. Brambilla, M., Ferrante, E., Birattari, M., & Dorigo, M. (2013). Swarm robotics: a review from the swarm engineering perspective. Swarm Intelligence, 7(1), 1-41.
    [CrossRef] [Google Scholar]
  15. Anderson, B. D., Fidan, B., Yu, C., & Walle, D. (2008). UAV formation control: Theory and application. In Recent advances in learning and control (pp. 15-33). Springer London.
    [CrossRef] [Google Scholar]
  16. Yan, Z., Jouandeau, N., & Cherif, A. A. (2013). A survey and analysis of multi-robot coordination. International Journal of Advanced Robotic Systems, 10(12), 399.
    [CrossRef] [Google Scholar]
  17. Zhang, R., Li, S., Ding, Y., Qin, X., & Xia, Q. (2022). UAV path planning algorithm based on improved Harris Hawks optimization. Sensors, 22(14), 5232.
    [CrossRef] [Google Scholar]
  18. Shao, Z., Yan, F., Zhou, Z., & Zhu, X. (2019). Path planning for multi-UAV formation rendezvous based on distributed cooperative particle swarm optimization. Applied Sciences, 9(13), 2621.
    [CrossRef] [Google Scholar]
  19. Zhang, J., Yan, J., & Zhang, P. (2020). Multi-UAV formation control based on a novel back-stepping approach. IEEE Transactions on Vehicular Technology, 69(3), 2437-2448.
    [CrossRef] [Google Scholar]
  20. Liu, Y., Zhang, X., Guan, X., & Delahaye, D. (2016). Adaptive sensitivity decision based path planning algorithm for unmanned aerial vehicle with improved particle swarm optimization. Aerospace Science and Technology, 58, 92-102.
    [CrossRef] [Google Scholar]
  21. Labbadi, M., & Cherkaoui, M. (2019). Robust adaptive backstepping fast terminal sliding mode controller for uncertain quadrotor UAV. Aerospace Science and Technology, 93, 105306.
    [CrossRef] [Google Scholar]
  22. Zhihao, C. A. I., Longhong, W. A. N. G., Jiang, Z. H. A. O., Kun, W. U., & Yingxun, W. A. N. G. (2020). Virtual target guidance-based distributed model predictive control for formation control of multiple UAVs. Chinese Journal of Aeronautics, 33(3), 1037-1056.
    [CrossRef] [Google Scholar]
  23. Wu, Y., Gou, J., Hu, X., & Huang, Y. (2020). A new consensus theory-based method for formation control and obstacle avoidance of UAVs. Aerospace Science and Technology, 107, 106332.
    [CrossRef] [Google Scholar]
  24. Huang, J., & Sun, W. (2020). A method of feasible trajectory planning for UAV formation based on bi-directional fast search tree. Optik, 221, 165213.
    [CrossRef] [Google Scholar]
  25. Shao, S., Peng, Y., He, C., & Du, Y. (2020). Efficient path planning for UAV formation via comprehensively improved particle swarm optimization. ISA transactions, 97, 415-430.
    [CrossRef] [Google Scholar]
  26. Liu, H., Meng, Q., Peng, F., & Lewis, F. L. (2020). Heterogeneous formation control of multiple UAVs with limited-input leader via reinforcement learning. Neurocomputing, 412, 63-71.
    [CrossRef] [Google Scholar]
  27. 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.
    [CrossRef] [Google Scholar]
  28. Frattolillo, F., Brunori, D., & Iocchi, L. (2023). Scalable and cooperative deep reinforcement learning approaches for multi-UAV systems: A systematic review. Drones, 7(4), 236.
    [CrossRef] [Google Scholar]
  29. López-González, A., Campaña, J. M., Martínez, E. H., & Contro, P. P. (2020). Multi robot distance based formation using Parallel Genetic Algorithm. Applied Soft Computing, 86, 105929.
    [CrossRef] [Google Scholar]
  30. Zhen, Z., Chen, Y., Wen, L., & Han, B. (2020). An intelligent cooperative mission planning scheme of UAV swarm in uncertain dynamic environment. Aerospace Science and Technology, 100, 105826.
    [CrossRef] [Google Scholar]
  31. Nath, A., & Niyogi, R. (2021, September). Distributed framework for task execution with quantitative skills. In International Conference on Computational Science and Its Applications (pp. 413-426). Cham: Springer International Publishing.
    [CrossRef] [Google Scholar]
  32. Huo, M., Duan, H., & Fan, Y. (2021). Pigeon-inspired circular formation control for multi-UAV system with limited target information. Guidance, Navigation and Control, 1(01), 2150004.
    [CrossRef] [Google Scholar]
  33. Azam, M. A., Mittelmann, H. D., & Ragi, S. (2021). Uav formation shape control via decentralized markov decision processes. Algorithms, 14(3), 91.
    [CrossRef] [Google Scholar]
  34. Qiang, F. E. N. G., Xingshuo, H. A. I., Bo, S. U. N., Yi, R. E. N., Zili, W. A. N. G., Dezhen, Y. A. N. G., ... & Ronggen, F. E. N. G. (2022). Resilience optimization for multi-UAV formation reconfiguration via enhanced pigeon-inspired optimization. Chinese Journal of Aeronautics, 35(1), 110-123.
    [CrossRef] [Google Scholar]
  35. Liang, D., Liu, Z., & Bhamra, R. (2022). Collaborative multi-robot formation control and global path optimization. Applied Sciences, 12(14), 7046.
    [CrossRef] [Google Scholar]
  36. Shin, J. J., & Bang, H. (2020). UAV path planning under dynamic threats using an improved PSO algorithm. International Journal of Aerospace Engineering, 2020(1), 8820284.
    [CrossRef] [Google Scholar]
  37. Wang, B. H., Wang, D. B., & Ali, Z. A. (2020). A Cauchy mutant pigeon-inspired optimization–based multi-unmanned aerial vehicle path planning method. Measurement and Control, 53(1-2), 83-92.
    [CrossRef] [Google Scholar]
  38. Qu, C., Gai, W., Zhong, M., & Zhang, J. (2020). A novel reinforcement learning based grey wolf optimizer algorithm for unmanned aerial vehicles (UAVs) path planning. Applied soft computing, 89, 106099.
    [CrossRef] [Google Scholar]
  39. Ge, F., Li, K., Han, Y., Xu, W., & Wang, Y. A. (2020). Path planning of UAV for oilfield inspections in a three-dimensional dynamic environment with moving obstacles based on an improved pigeon-inspired optimization algorithm. Applied Intelligence, 50, 2800-2817.
    [CrossRef] [Google Scholar]
  40. Phung, M. D., Quach, C. H., Dinh, T. H., & Ha, Q. (2017). Enhanced discrete particle swarm optimization path planning for UAV vision-based surface inspection. Automation in Construction, 81, 25-33.
    [CrossRef] [Google Scholar]
  41. Phung, M. D., & Ha, Q. P. (2021). Safety-enhanced UAV path planning with spherical vector-based particle swarm optimization. Applied Soft Computing, 107, 107376.
    [CrossRef] [Google Scholar]
  42. Wu, Y., Low, K. H., Pang, B., & Tan, Q. (2021). Swarm-based 4D path planning for drone operations in urban environments. IEEE transactions on vehicular technology, 70(8), 7464-7479.
    [CrossRef] [Google Scholar]
  43. Yahia, H. S., & Mohammed, A. S. (2023). Path planning optimization in unmanned aerial vehicles using meta-heuristic algorithms: A systematic review. Environmental Monitoring and Assessment, 195(1), 30.
    [CrossRef] [Google Scholar]
  44. Chen, J., Zhao, H., & Wang, L. (2021, March). Three dimensional path planning of UAV based on adaptive particle swarm optimization algorithm. In Journal of Physics: Conference Series (Vol. 1846, No. 1, p. 012007). IOP Publishing.
    [CrossRef] [Google Scholar]
  45. Li, J., Xiong, Y., & She, J. (2021, March). An improved ant colony optimization for path planning with multiple UAVs. In 2021 IEEE International Conference on Mechatronics (ICM) (pp. 1-5). IEEE.
    [CrossRef] [Google Scholar]
  46. He, W., Qi, X., & Liu, L. (2021). A novel hybrid particle swarm optimization for multi-UAV cooperate path planning. Applied Intelligence, 51(10), 7350-7364.
    [CrossRef] [Google Scholar]
  47. Ji, Y., Zhao, X., & Hao, J. (2022). A Novel UAV Path Planning Algorithm Based on Double‐Dynamic Biogeography‐Based Learning Particle Swarm Optimization. Mobile Information Systems, 2022(1), 8519708.
    [CrossRef] [Google Scholar]
  48. Shafiq, M., Ali, Z. A., Israr, A., Alkhammash, E. H., Hadjouni, M., & Jussila, J. J. (2022). Convergence analysis of path planning of multi-UAVs using max-min ant colony optimization approach. Sensors, 22(14), 5395.
    [CrossRef] [Google Scholar]
  49. Ali, Z. A., Zhangang, H., & Zhengru, D. (2023). Path planning of multiple UAVs using MMACO and DE algorithm in dynamic environment. Measurement and Control, 56(3-4), 459-469.
    [CrossRef] [Google Scholar]
  50. Teng, H., Ahmad, I., Msm, A., & Chang, K. (2020). 3D optimal surveillance trajectory planning for multiple UAVs by using particle swarm optimization with surveillance area priority. IEEE Access, 8, 86316-86327.
    [CrossRef] [Google Scholar]
  51. Selma, B., Chouraqui, S., & Abouaïssa, H. (2020). Fuzzy swarm trajectory tracking control of unmanned aerial vehicle. Journal of Computational Design and Engineering, 7(4), 435-447.
    [CrossRef] [Google Scholar]
  52. Rubí, B., Pérez, R., & Morcego, B. (2020). A survey of path following control strategies for UAVs focused on quadrotors. Journal of Intelligent & Robotic Systems, 98(2), 241-265.
    [CrossRef] [Google Scholar]
  53. Selma, B., Chouraqui, S., & Abouaïssa, H. (2020). Optimal trajectory tracking control of unmanned aerial vehicle using ANFIS-IPSO system. International Journal of Information Technology, 12(2), 383-395.
    [CrossRef] [Google Scholar]
  54. AbdulSamed, B. N., Aldair, A. A., & Al-Mayyahi, A. (2020). Robust trajectory tracking control and obstacles avoidance algorithm for quadrotor unmanned aerial vehicle. Journal of Electrical Engineering & Technology, 15(2), 855-868.
    [CrossRef] [Google Scholar]
  55. Madridano, Á., Al-Kaff, A., Martín, D., & De La Escalera, A. (2021). Trajectory planning for multi-robot systems: Methods and applications. Expert Systems with Applications, 173, 114660.
    [CrossRef] [Google Scholar]
  56. Selma, B., Chouraqui, S., Selma, B., & Abouaïssa, H. (2021). ANFIS controller design based on pigeon-inspired optimization to control an UAV trajectory tracking task. Iran Journal of Computer Science, 4(1), 1-16.
    [CrossRef] [Google Scholar]
  57. Telli, K., Kraa, O., Himeur, Y., Ouamane, A., Boumehraz, M., Atalla, S., & Mansoor, W. (2023). A comprehensive review of recent research trends on unmanned aerial vehicles (uavs). Systems, 11(8), 400.
    [CrossRef] [Google Scholar]
  58. Khan, S. I., Qadir, Z., Munawar, H. S., Nayak, S. R., Budati, A. K., Verma, K. D., & Prakash, D. (2021). UAVs path planning architecture for effective medical emergency response in future networks. Physical Communication, 47, 101337.
    [CrossRef] [Google Scholar]
  59. Qadir, Z., Zafar, M. H., Moosavi, S. K. R., Le, K. N., & Mahmud, M. P. (2021). Autonomous UAV path-planning optimization using metaheuristic approach for predisaster assessment. IEEE Internet of Things Journal, 9(14), 12505-12514.
    [CrossRef] [Google Scholar]
  60. Shao, S., He, C., Zhao, Y., & Wu, X. (2021). Efficient trajectory planning for UAVs using hierarchical optimization. IEEE Access, 9, 60668-60681.
    [CrossRef] [Google Scholar]
  61. Navabi, M., Davoodi, A., & Mirzaei, H. (2022). Trajectory tracking of under-actuated quadcopter using Lyapunov-based optimum adaptive controller. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 236(1), 202-215.
    [CrossRef] [Google Scholar]
  62. Chung, S. J., Paranjape, A. A., Dames, P., Shen, S., & Kumar, V. (2018). A survey on aerial swarm robotics. IEEE Transactions on robotics, 34(4), 837-855.
    [CrossRef] [Google Scholar]
  63. Mir, I., Gul, F., Mir, S., Khan, M. A., Saeed, N., Abualigah, L., ... & Gandomi, A. H. (2022). A survey of trajectory planning techniques for autonomous systems. Electronics, 11(18), 2801.
    [CrossRef] [Google Scholar]
  64. Javed, S., Hassan, A., Ahmad, R., Ahmed, W., Ahmed, R., Saadat, A., & Guizani, M. (2024). State-of-the-art and future research challenges in UAV swarms. IEEE Internet of Things Journal, 11(11), 19023-19045.
    [CrossRef] [Google Scholar]

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APA Style
Abro, G. E. M., Ali, Z. A., & Masood, R. J. (2024). Synergistic UAV Motion: A Comprehensive Review on Advancing Multi-Agent Coordination. ICCK Transactions on Sensing, Communication, and Control, 1(2), 72-88. https://doi.org/10.62762/TSCC.2024.211408
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TY  - JOUR
AU  - Abro, Ghulam E Mustafa
AU  - Ali, Zain Anwar
AU  - Masood, Rana Javed
PY  - 2024
DA  - 2024/10/29
TI  - Synergistic UAV Motion: A Comprehensive Review on Advancing Multi-Agent Coordination
JO  - ICCK Transactions on Sensing, Communication, and Control
T2  - ICCK Transactions on Sensing, Communication, and Control
JF  - ICCK Transactions on Sensing, Communication, and Control
VL  - 1
IS  - 2
SP  - 72
EP  - 88
DO  - 10.62762/TSCC.2024.211408
UR  - https://www.icck.org/article/abs/TSCC.2024.211408
KW  - collective motion
KW  - dynamic agent systems
KW  - formation control
KW  - path planning and swarm intelligence
AB  - Collective motion has been a pivotal area of research, especially due to its substantial importance in Unmanned Aerial Vehicle (UAV) systems for several purposes, including path planning, formation control, and trajectory tracking. UAVs significantly enhance coordination, flexibility, and operational efficiency in practical applications such as search-and-rescue operations, environmental monitoring, and smart city construction. Notwithstanding the progress in UAV technology, significant problems persist, especially in attaining dependable and effective coordination in intricate, dynamic, and unexpected settings. This study offers a comprehensive examination of the fundamental principles, models, and tactics employed to comprehend and regulate collective motion in UAV systems. This paper methodically analyses recent breakthroughs, exposes deficiencies in existing approaches, and emphasises case studies demonstrating the practical application of collective motion. The survey examines the substantial practical effects of collective motion on improving UAV operations, emphasizing scalability, resilience, and adaptability. This review is significant for its potential to inform future research and practical applications. It seeks to provide a systematic framework for the advancement of more resilient and scalable UAV collaboration models, aiming to tackle the ongoing challenges in the domain. The insights offered are essential for academics and practitioners aiming to enhance UAV collaboration in dynamic environments, facilitating the development of more sophisticated, flexible, and mission-resilient multi-UAV systems. This study is set to significantly advance UAV technology, having extensive ramifications for several industries.
SN  - 3068-9287
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 
BibTeX Format
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@article{Abro2024Synergisti,
  author = {Ghulam E Mustafa Abro and Zain Anwar Ali and Rana Javed Masood},
  title = {Synergistic UAV Motion: A Comprehensive Review on Advancing Multi-Agent Coordination},
  journal = {ICCK Transactions on Sensing, Communication, and Control},
  year = {2024},
  volume = {1},
  number = {2},
  pages = {72-88},
  doi = {10.62762/TSCC.2024.211408},
  url = {https://www.icck.org/article/abs/TSCC.2024.211408},
  abstract = {Collective motion has been a pivotal area of research, especially due to its substantial importance in Unmanned Aerial Vehicle (UAV) systems for several purposes, including path planning, formation control, and trajectory tracking. UAVs significantly enhance coordination, flexibility, and operational efficiency in practical applications such as search-and-rescue operations, environmental monitoring, and smart city construction. Notwithstanding the progress in UAV technology, significant problems persist, especially in attaining dependable and effective coordination in intricate, dynamic, and unexpected settings. This study offers a comprehensive examination of the fundamental principles, models, and tactics employed to comprehend and regulate collective motion in UAV systems. This paper methodically analyses recent breakthroughs, exposes deficiencies in existing approaches, and emphasises case studies demonstrating the practical application of collective motion. The survey examines the substantial practical effects of collective motion on improving UAV operations, emphasizing scalability, resilience, and adaptability. This review is significant for its potential to inform future research and practical applications. It seeks to provide a systematic framework for the advancement of more resilient and scalable UAV collaboration models, aiming to tackle the ongoing challenges in the domain. The insights offered are essential for academics and practitioners aiming to enhance UAV collaboration in dynamic environments, facilitating the development of more sophisticated, flexible, and mission-resilient multi-UAV systems. This study is set to significantly advance UAV technology, having extensive ramifications for several industries.},
  keywords = {collective motion, dynamic agent systems, formation control, path planning and swarm intelligence},
  issn = {3068-9287},
  publisher = {Institute of Central Computation and Knowledge}
}

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