IECE Transactions on Sensing, Communication, and Control
ISSN: 3065-7431 (Online) | ISSN: 3065-7423 (Print)
Email: [email protected]
[1] Elgeti, J., Winkler, R. G., & Gompper, G. (2015). Physics of microswimmers—single particle motion and collective behavior: a review. Reports on progress in physics, 78(5), 056601.
[2] Garnier, S., Gautrais, J., & Theraulaz, G. (2007). The biological principles of swarm intelligence. Swarm intelligence, 1, 3-31.
[3] Queralta, J. P., Taipalmaa, J., Pullinen, B. C., Sarker, V. K., Gia, T. N., Tenhunen, H., ... & Westerlund, T. (2020). Collaborative multi-robot systems for search and rescue: Coordination and perception. arXiv preprint arXiv:2008.12610.
[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.
[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.
[6] Vicsek, T., & Zafeiris, A. (2012). Collective motion. Physics Reports, 517(3–4), 71–140.
[7] Everett, M., Chen, Y. F., & How, J. P. (2018, October). Motion planning among dynamic, decision-making agents with deep reinforcement learning. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 3052-3059). IEEE.
[8] Luo, Q., & Duan, H. (2017). Distributed UAV flocking control based on homing pigeon hierarchical strategies. Aerospace Science and Technology, 70, 257-264.
[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.
[10] Bode, N. W., Franks, D. W., & Wood, A. J. (2011). Limited interactions in flocks: relating model simulations to empirical data. Journal of The Royal Society Interface, 8(55), 301-304.
[11] Biro, D., Sasaki, T., & Portugal, S. J. (2016). Bringing a time–depth perspective to collective animal behaviour. Trends in ecology & evolution, 31(7), 550-562.
[12] Hetz, S. K., & Bradley, T. J. (2005). Insects breathe discontinuously to avoid oxygen toxicity. Nature, 433(7025), 516-519.
[13] Kearns, D. B. (2010). A field guide to bacterial swarming motility. Nature reviews microbiology, 8(9), 634-644.
[14] Olson, R. S., Hintze, A., Dyer, F. C., Knoester, D. B., & Adami, C. (2013). Predator confusion is sufficient to evolve swarming behaviour. Journal of The Royal Society Interface, 10(85), 20130305.
[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.
[16] Zhang, Y. Q., Wang, J. H., Wang, Y., Jia, Z. C., Sun, Q., Pei, Q. Y., & Wu, D. (2024). Intelligent planning of fire evacuation routes in buildings based on improved adaptive ant colony algorithm. Computers & Industrial Engineering, 194, 110335.
[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.
[18] 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.
[19] Wang, L., Zhai, Z., Zhu, Z., & Mao, E. (2022, January). Path tracking control of an autonomous tractor using improved Stanley controller optimized with multiple-population genetic algorithm. In Actuators (Vol. 11, No. 1, p. 22). MDPI.
[20] Sharma, A., Shoval, S., Sharma, A., & Pandey, J. K. (2022). Path planning for multiple targets interception by the swarm of UAVs based on swarm intelligence algorithms: A review. IETE Technical Review, 39(3), 675-697.
[21] Israr, A., Ali, Z. A., Alkhammash, E. H., & Jussila, J. J. (2022). Optimization methods applied to motion planning of unmanned aerial vehicles: A review. Drones, 6(5), 126.
[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.
[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.
[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.
[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.
[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.
[27] Lizzio, F. F., Capello, E., & Guglieri, G. (2021, June). A review of consensus-based multi-agent UAV applications. In 2021 International Conference on Unmanned Aircraft Systems (ICUAS) (pp. 1548-1557). IEEE.
[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.
[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.
[30] Nath, A., Arun, A. R., & Niyogi, R. (2020). DMTF: A Distributed Algorithm for Multi-team Formation. In ICAART (1) (pp. 152-160).
[31] Nath, A., & Niyogi, R. (2021). Distributed framework for task execution with quantitative skills. In Computational Science and Its Applications–ICCSA 2021: 21st International Conference, Cagliari, Italy, September 13–16, 2021, Proceedings, Part VII 21 (pp. 413-426). Springer International Publishing.
[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.
[33] Azam, M. A., Mittelmann, H. D., & Ragi, S. (2021). Uav formation shape control via decentralized markov decision processes. Algorithms, 14(3), 91.
[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.
[35] Liang, D., Liu, Z., & Bhamra, R. (2022). Collaborative multi-robot formation control and global path optimization. Applied Sciences, 12(14), 7046.
[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.
[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.
[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.
[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.
[40] Das, P. K., & Jena, P. K. (2020). Multi-robot path planning using improved particle swarm optimization algorithm through novel evolutionary operators. Applied Soft Computing, 92, 106312.
[41] Che, G., Liu, L., & Yu, Z. (2020). An improved ant colony optimization algorithm based on particle swarm optimization algorithm for path planning of autonomous underwater vehicle. Journal of Ambient Intelligence and Humanized Computing, 11(8), 3349-3354.
[42] Ajeil, F. H., Ibraheem, I. K., Azar, A. T., & Humaidi, A. J. (2020). Grid-based mobile robot path planning using aging-based ant colony optimization algorithm in static and dynamic environments. Sensors, 20(7), 1880.
[43] Shafiq, M., Ali, Z. A., & Alkhammash, E. H. (2021). A cluster-based hierarchical-approach for the path planning of swarm. Applied Sciences, 11(15), 6864.
[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.
[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.
[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.
[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.
[48] Jiang, S., Yue, Y., Chen, C., Chen, Y., & Cao, L. (2024). A multi-objective optimization problem solving method based on improved golden jackal optimization algorithm and its application. Biomimetics, 9(5), 270.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[60] Shao, S., He, C., Zhao, Y., & Wu, X. (2021). Efficient trajectory planning for UAVs using hierarchical optimization. IEEE Access, 9, 60668-60681.
[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.
[62] Wang, S., Li, G., Song, J., & Liu, B. (2024). Research on an Intelligent Vehicle Trajectory Tracking Method Based on Optimal Control Theory. World Electric Vehicle Journal, 15(4), 160.
[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.
[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.
IECE Transactions on Sensing, Communication, and Control
ISSN: 3065-7431 (Online) | ISSN: 3065-7423 (Print)
Email: [email protected]
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