[1] T. Lei, T. Sellers, C. Luo, D.W. Carruth and Z. Bi, Graph-based robot optimal path planning with bio-inspired algorithms, Biomimetic Intelligence and Robotics, Vol. 3, No. 3, p. 100119, 2023.
[2] J. Kim, Three-dimensional formation control for robot swarms, Applied Sciences, Vol. 12, No. 16, 2021.
[3] M. Zarourati, M. Mirshams and M. Tayefi, Attitude path design and adaptive robust tracking control of a remote sensing satellite in various imaging modes, Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, Vol. 237, No. 9, pp. 2166-2184, 2023.
[4] M. Zarourati, M. Mirshams and M. Tayefi, Designing an adaptive robust observer for underactuation fault diagnosis of a remote sensing satellite, International Journal of Adaptive Control and Signal Processing, Vol. 37, No. 11, pp. 2812-2834, 2023.
[5] M. Zarourati, M. Mirshams and M. Tayefi, Active underactuation fault-tolerant backstepping attitude tracking control of a satellite with interval error constraints, Advanced Control for Applications, Vol. 6, No. 3, p. 215, 2024.
[6] S.K. Baduge, S. Jayasuriya, R.K. Pandey, M.K. Hettiarachchi, D. Amaratunga and C.A. Hewage, Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications, Automation in Construction, Vol. 141, p. 104440, 2022.
[7] H. Sharma, A. Haque and F. Blaabjerg, Machine learning in wireless sensor networks for smart cities: A survey, Electronics, Vol. 10, p. 1012, 2021.
[8] R. Ke, Y. Zhuang, Z. Pu and Y. Wang, A smart, efficient, and reliable parking surveillance system with edge artificial intelligence on IoT devices, IEEE Transactions on Intelligent Transportation Systems, Vol. PP, pp. 1-13, 2020.
[9] J. Pirkandi, M.S. Abdollahpour, H. Parhizkar and M. Mahmoodi, Numerical modeling of air distribution in the air conditioning system of a manned aircraft, Aerospace Science and Technology, Vol. 12, No. 1, pp. 239-253, 2023. (In Persian).
[10] P. Guo, R. Zhang and B. Xu, Safety separation distance design for UAV formation based on system performance, IEEE Transactions on Aerospace and Electronic Systems, pp. 1-16, 2025.
[11] M.E. Elshaar, M.R. Elbalshy, A. Hussien and M. Abido, Path planning in a dynamic environment using spherical particle swarm optimization, 2024 IEEE Congress on Evolutionary Computation (CEC), pp. 1-8, 2024.
[12] S. Lin, J. Wang, B. Huang, X. Kong and H. Yang, Bio particle swarm optimization and reinforcement learning algorithm for path planning of automated guided vehicles in dynamic industrial environments, Scientific Reports, Vol. 15, No. 1, p. 463, 2025.
[13] S. Poudel, M.Y. Arafat and S. Moh, Bio-inspired optimization-based path planning algorithms in unmanned aerial vehicles: A survey, Sensors, Vol. 23, No. 6, 2021.
[14] W. Harris, S. Tseng, T. Viso, M. Weissman and C.-K. Ngan, Swarm intelligence path-planning pipeline and algorithms for UAVs: Simulation, analysis, and recommendation, 2024.
[15] S. Si-Ma, X. Liu, Y. Zhang, Q. Wu and J. Gao, Efficient maximum iterations for swarm intelligence algorithms: A comparative study, Artificial Intelligence Review, Vol. 58, No. 3, p. 87, 2025.
[16] P. Duraisamy, M.N. Santhanakrishnan and R. Amirtharajan, Genetic algorithm optimized grey-box modelling and fuzzy logic controller for tail-actuated robotic fish, Neural Processing Letters, Vol. 55, No. 8, pp. 11577-11594, 2023.
[17] Z. Jiang, Optimal design of fuzzy controller for underwater robot (UR) based on improved PSO algorithm, Proceedings of the 2nd International Conference on Cognitive Based Information Processing and Applications (CIPA 2022), Singapore, B.J. Jansen, Q. Zhou and J. Ye, Eds., Springer Nature Singapore, pp. 577-586, 2022.
[18] I. Shafieenejad, E.D. Rouzi, J. Sardari, M.S. Araghi, A. Esmaeili and S. Zahedi, Fuzzy logic, neural-fuzzy network and honey bees algorithm to develop the swarm motion of aerial robots, Evolving Systems, Vol. 13, No. 2, pp. 319-330, 2022.
[19] I. Shafieenejad, A. Cheraghi and M. Tafreshi, Rescue mission designing for lifesaving based on a new aerial vehicle using imperialist competition algorithm, International Journal of Swarm Intelligence and Evolutionary Computation, Vol. 6, No. 153, p. 2, 2017.
[20] M. Samani, M. Tafreshi, I. Shafieenejad and A.A. Nikkhah, Minimum-time open-loop and closed-loop optimal guidance with GA-PSO and neural fuzzy for Samarai MAV flight, IEEE Aerospace and Electronic Systems Magazine, Vol. 30, No. 5, pp. 28-37, 2015.
[21] E.C. Ozkat, Vibration data-driven anomaly detection in UAVs: A deep learning approach, Engineering Science and Technology, an International Journal, Vol. 54, p. 101702, 2024.
[22] M. Hosseini, M. Nosratollahi and H. Sadati, Multidisciplinary design optimization of unmanned aerial vehicle under uncertainty, Journal of Aerospace Technology and Management, Vol. 9, No. 2, pp. 169-178, 2017.
[23] A. Maitra, S.R. Prasath and R. Padhi, A brief survey on bio-inspired algorithms for autonomous landing, Vol. 49, No. 1, pp. 407-412, 2016.
[24] K. Hou, H. Sun, Q. Jia, Y. Zhang, N. Wei and L. Meng, Analysis and design of spherical aerial vehicle's motion modes, Applied Mechanics and Materials, Vol. 411-414, pp. 1836-1839, 2013.
[25] H. Sun, K. Hou and Q. Jia, Development, analysis and control of a spherical aerial vehicle, Journal of Vibroengineering, Vol. 15, pp. 1069-1080, 2013.
[26] X. Olaz, D. Alaez, M. Prieto, J. Villadangos and J.J. Astrain, Quadcopter neural controller for take-off and landing in windy environments, Expert Systems with Applications, Vol. 225, p. 120146, 2023.
[27] K. Malandrakis, R. Dixon, A. Savvaris and A. Tsourdos, Design and development of a novel spherical UAV, IFAC-PapersOnLine, Vol. 49, No. 17, pp. 320-325, 2016.
[28] D. Kim and S. Yang, Center-of-gravity variation-driven spherical UAV system and its control law, International Journal of Aerospace Engineering, Vol. 2020, No. 1, p. 5754205, 2020.
[29] M. Bowkett, K. Thanapalan and E. Constant, Operational safety analysis and controller design of a dual drones system, in 2017 International Symposium on Computer Science and Intelligent Controls (ISCSIC), 20-22 Oct. 2017.
[30] V.H. Dominguez, O. Garcia-Salazar, L. Amezquita-Brooks, L.A. Reyes-Osorio, C. Santana-Delgado and E.G. Rojo-Rodriguez, Micro coaxial drone: Flight dynamics, simulation and ground testing, Aerospace, Vol. 9, No. 5, 2021.
[31] X. Ai, Y. Zhang and Y.-Y. Chen, Spherical formation tracking control of non-holonomic UAVs with state constraints and time delays, Aerospace, Vol. 10, No. 2, 2018.
[32] W.K. Loh and J. Jacob, Modeling and attitude control analysis of a spherical VTOL aerial vehicle, in 51st AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition, Aerospace Sciences Meetings: American Institute of Aeronautics and Astronautics, 2013.
[33] S. Das and V. Kumar, Navigating the swarm: Bio-inspired robotics, intelligent algorithms, and applications, 2024.
[34] A. Juarez-Lora and A. Rodriguez-Angeles, Bio-inspired autonomous navigation and formation controller for differential mobile robots, Entropy, Vol. 25, No. 4, 2023.
[35] M.H. Roni, M. Rana, H. Pota, M.M. Hasan and M.S. Hussain, Recent trends in bio-inspired meta-heuristic optimization techniques in control applications for electrical systems: A review, International Journal of Dynamics and Control, pp. 1-13, 2022.
[36] S. Lin, A. Liu, J. Wang and X. Kong, A review of path-planning approaches for multiple mobile robots, Machines, Vol. 10, No. 9, 2019.
[37] F. Wang, D.F. Araújo and Y.-F. Li, Reliability assessment of autonomous vehicles based on the safety control structure, Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, Vol. 237, No. 2, pp. 389-404, 2023.
[38] A.A. Golroudbari and M.H. Sabour, Recent advancements in deep learning applications and methods for autonomous navigation: A comprehensive review, arXiv preprint arXiv:2302.11089, 2023.
[39] L. Yang, X. Liu, S. Zhang and Y. Chen, Path planning technique for mobile robots: A review, Machines, Vol. 11, No. 10, 2020.
[40] Y. Huang, Y. Li, Z. Zhang and Q. Sun, A novel path planning approach for AUV based on improved whale optimization algorithm using segment learning and adaptive operator selection, Ocean Engineering, Vol. 280, p. 114591, 2023.
[41] M. Shahbaz and A. Khan, Autonomous navigation of swarms in 3D environments using deep reinforcement learning, 2020 International Symposium on Recent Advances in Electrical Engineering & Computer Sciences (RAEE & CS), Vol. 5, pp. 1-6, 2020.
[42] H. Xiangwang and S. Xiaofeng, Trajectory optimization of connected and autonomous vehicles at a multilane freeway merging area, ResearchGate, 2019.
[43] N. Haefner, J. Wincent, V. Parida and O. Gassmann, Artificial intelligence and innovation management: A review, framework, and research agenda, Technological Forecasting and Social Change, Vol. 162, p. 120392, 2021.
[44] H. Buchbinder and J. Newson, Social knowledge and market knowledge: universities in the information age, in Higher education in the information age: Routledge, pp. 21-31, 2021.
[45] S. Satapathy and A. Naik, Social group optimization (SGO): A new population evolutionary optimization technique, Vol. 2, pp. 203, 2016.
[46] N. SinghPal and S. Sharma, Robot path planning using swarm intelligence: A survey, International Journal of Computer Applications, Vol. 83, pp. 5-12, 2013.
[47] Y. Weng, J. Cao and Z. Chen, Global optimization of optimal Delaunay triangulation with modified whale optimization algorithm, Engineering with Computers, 2024.
[48] I.Y. Fister Jr., Xin-She Fister, Dušan Fister, Janez Brest and Iztok Fister, A comprehensive review of whale optimization algorithms: Variants, hybrids, and applications, Artificial Intelligence Review, 2023.
[49] T. Wang, Z. Xin, H. Miao, H. Zhang, Z. Chen and Y. Du, Optimal trajectory planning of grinding robot based on improved whale optimization algorithm, Mathematical Problems in Engineering, Vol. 2020, No. 1, p. 3424313, 2020.
[50] M.I. Khaleel, M. Safran, S. Alfarhood and M. Zhu, Energy-latency trade-off analysis for scientific workflow in cloud environments: The role of processor utilization ratio and mean grey wolf optimizer, Engineering Science and Technology, an International Journal, Vol. 50, p. 101611, 2024.
[51] L. Ratnabala, R. Peter and E.Y.A. Charles, Evolutionary swarm robotics: Dynamic subgoal-based path formation and task allocation for exploration and navigation in unknown environments, Journal of Swarm Robotics Research, Vol. 11, No. 7, pp. 635-643, 2024.
[52] E. Hancer, Artificial bee colony: Theory, literature review, and application in image segmentation, in Recent Advances on Memetic Algorithms and its Applications in Image Processing, D.J. Hemanth, B.V. Kumar and G.R.K. Manavalan, Eds., Singapore: Springer Singapore, pp. 47-67, 2020.
[53] K.M. Werner, H. Haslob, A.F. Reichel, A. Gimpel and V. Stelzenmüller, Offshore wind farm foundations as artificial reefs: The devil is in the detail, Fisheries Research, Vol. 272, p. 106937, 2024.
[54] J. Wang, W.C. Wang, X.X. Hu, L. Qiu and H.F. Zang, Black‑winged kite algorithm: A nature‑inspired meta‑heuristic for solving benchmark functions and engineering problems, Artificial Intelligence Review, Vol. 57, p. 98, 2024.
[55] W. Li, The application of artificial intelligence in aerospace engineering, Applied and Computational Engineering, Vol. 35, pp. 17-25, 2024.
[56] H. Zhang, S. Yao, S. Zhang, J. Leng, L. Wei and Q. Liu, A block-based heuristic search algorithm for the two-dimensional guillotine strip packing problem, Engineering Applications of Artificial Intelligence, Vol. 134, p. 108624, 2024.
[57] J. Smith and E. Davis, AI-based multi-objective optimization for aircraft wing design, Engineering Applications of Artificial Intelligence, Vol. 106, No. 3, pp. 345-367, 2023.
[58] S. Saremi, S. Mirjalili and A. Lewis, Grasshopper optimisation algorithm: Theory and application, Advances in Engineering Software, Vol. 105, pp. 30-47, 2017.
[59] J. Dong, J. Shi, Z. Ma and T. Yu, Research of the FLC + PID switching control strategy based on real-time error for the pneumatic polishing force regulating system, Engineering Science and Technology, an International Journal, Vol. 51, p. 101659, 2024.
[60] S.R. Das, P. Kumar, M. Patel, R. Singh and A. Verma, Fuzzy controller designed based multilevel inverter for power quality enhancement, IEEE Transactions on Consumer Electronics, pp. 1-1, 2024, doi: 10.1109/TCE.2024.3389687.
[61] C. Ardil, Fighter aircraft selection using fuzzy preference optimization programming (POP), International Journal of Aerospace and Mechanical Engineering, Vol. 16, No. 10, pp. 279-290, 2022.
[62] J.M. Sánchez-Lozano and O.N. Rodríguez, Application of fuzzy reference ideal method (FRIM) to the military advanced training aircraft selection, Applied Soft Computing, Vol. 88, p. 106061, 2020.