M.A. Abitha and Abdul Saleem


  1. [1] A. Gasparetto, P. Boscariol, A. Lanzutti, and R. Vidoni,Path planning and trajectory planning algorithms: A generaloverview, in Motion and operation planning of robotic systems.(Cham: Springer, 2015), 3–27.
  2. [2] H.J. Asl, G. Oriolo, and H. Bolandi, An adaptive schemefor image-based visual servoing of an underactuated UAV,International Journal of Robotics and Automation, 29(1),2014, 92–104. [Online]. Available: https://doi.org/10.2316/journal.206.2014.1.206-3942
  3. [3] D. Burke, A. Chapman, and I. Shames, Fast spline trajectoryplanning: Minimum snap and beyond, 2021. [Online]. Available:https://arxiv.org/abs/2105.01788
  4. [4] D. Eager, A.-M. Pendrill, and N. Reistad, Beyond velocityand acceleration: Jerk, snap and higher derivatives, EuropeanJournal of Physics, 37(6), 2016, 65008
  5. [5] C. An, B. Li, W. Shi, and X. Zhang, Autonomous quadrotorUAV systems for dynamic platform landing with onboardsensors, International Journal of Robotics and Automation, 38,2023, 296–305. [Online]. Available: https://doi.org/10.2316/j.2023.2060807
  6. [6] C. Kownacki, Successful application of miniature laserrangefinders in obstacle avoidance method for fixed wing MAV,International Journal of Robotics and Automation, 28(3),2013, 292–298. [Online]. Available: https://doi.org/10.2316/journal.206.2013.3.206-3936
  7. [7] J.N. Yasin, S.A.S. Mohamed, M.-H. Haghbayan, J. Heikkonen,H. Tenhunen, and J. Plosila, Unmanned aerial vehicles (UAVs):Collision avoidance systems and approaches, IEEE Access,8, 2020, 105139–105155. [Online]. Available: https://doi.org/10.1109/access.2020.3000064
  8. [8] S. Challa, M.R. Morelande, D. Musicki, and R.J. Evans,Fundamentals of object tracking. (Cambridge: CambridgeUniversity Press, 2009). [Online]. Available: https://doi.org/10.1017/cbo9780511975837.
  9. [9] H. Kaur and J.S. Sahambi, Vehicle tracking in video usingfractional feedback Kalman filter, IEEE Transactions onComputational Imaging, 2(4), 2016, 550–561.
  10. [10] H.A. Patel and D.G. Thakore, Moving object tracking usingKalman filter, International Journal of Computer Science andMobile Computing, 2(4), 2013, 326–332.
  11. [11] F. Gustafsson, Particle filter theory and practice withpositioning applications, IEEE Aerospace and ElectronicSystems Magazine, 25(7), 2010, 53–82.
  12. [12] A. Loquercio, A.I. Maqueda, C.R. Del-Blanco, and D.Scaramuzza, DroNet: Learning to fly by driving, IEEE Roboticsand Automation Letters, 3(2), 2018, 1088–1095.
  13. [13] R. Xie, Z. Meng, L. Wang, H. Li, K. Wang, and Z. Wu,Unmanned aerial vehicle path planning algorithm basedon deep reinforcement learning in large-scale and dynamicenvironments, IEEE Access, 9, 2021, 24884–24900.
  14. [14] T. Chettibi, Generating near-optimal reference trajectoriesfor small fixed-wing UAVs, International Journal of Roboticsand Automation, 26(2), 2011, 187. [Online]. Available:https://doi.org/10.2316/journal.206.2011.2.206-3415
  15. [15] C. Richter, A. Bry, and N. Roy, Polynomial trajectoryplanning for aggressive quadrotor flight in dense indoorenvironments, in Robotics Research. (Cham: Springer, 2016),649–666.
  16. [16] Y. Yan, Z. Lv, J. Yuan, and S. Zhang, Obstacle avoidancefor multi-UAV system with optimized artificial potential fieldalgorithm, International Journal of Robotics and Automation,36(10), 2021, 1–7.
  17. [17] J. Li, B. Xu, Y. Yang, and H. Wu, Three-phase qubits-basedquantum ant colony optimization algorithm for path planningof automated guided vehicles, International Journal of Roboticsand Automation, 34(2), 2019, 156–163. [Online]. Available:https://doi.org/10.2316/j.2019.206-5206
  18. [18] L. Quan, L. Han, B. Zhou, S. Shen, and F. Gao, Survey ofUAV motion planning, IET Cybersystems and Robotics, 2(1),2020, 14–21.
  19. [19] P. He and S. Dai, Real-time stealth corridor path planning forfleets of unmanned aerial vehicles in low-altitude penetration,International Journal of Robotics and Automation, 30(1), 2015,60–69.
  20. [20] B. Zhang and H. Duan, Three-dimensional path planningfor uninhabited combat aerial vehicle based on predator-prey pigeon-inspired optimization in dynamic environment,IEEE/ACM Transactions on Computational Biology andBioinformatics, 14(1), 2017, 97–107. 15
  21. [21] Z. Xue and X. Liu, Trajectory planning of unmanned aerialvehicle based on the improved biogeography-based optimizationalgorithm, Advances in Mechanical Engineering, 13(3), 2021,16878140211004295.
  22. [22] H. Hu, Y. Wu, J. Xu, and Q. Sun, Cuckoo search-based methodfor trajectory planning of quadrotor in an urban environment,487Proceedings of the Institution of Mechanical Engineers, Part G:Journal of Aerospace Engineering, 233(12), 2019, 4571–4582.
  23. [23] B. Galvan, D. Greiner, J. P´eriaux, M. S´efrioui, andG. Winter, Parallel evolutionary computation for solvingcomplex CFD optimization problems : A review and somenozzle applications, in K. Matsuno, A. Ecer, N. Satofuka,J. Periaux, and P. Fox (eds.), Parallel computationalfluid dynamics. (Amsterdam: North-Holland, 2003), 573–604.[Online]. Available: https://www.sciencedirect.com/science/article/pii/B9780444506801500723
  24. [24] D. Mellinger and V. Kumar, Minimum snap trajectory gen-eration and control for quadrotors, Proc. IEEE InternationalConf. on Robotics and Automation, Shanghai, 2011, 2520–2525.
  25. [25] B. Salamat and A. M. Tonello, Stochastic trajectory generationusing particle swarm optimization for quadrotor unmannedaerial vehicles (UAVs), Aerospace, 4(2), 2017, 27. [Online].Available: https://www.mdpi.com/2226-4310/4/2/27
  26. [26] X. Jiang, L. Zhang, and X.M. Chen, Short-term forecasting ofhigh-speed rail demand: A hybrid approach combining ensembleempirical mode decomposition and gray support vector machinewith real-world applications in China, Transportation ResearchPart C: Emerging Technologies, 44, 110–127, 2014.
  27. [27] K.R. Harrison, A.P. Engelbrecht, and B.M. Ombuki-Berman,Self-adaptive particle swarm optimization: A review andanalysis of convergence, Swarm Intelligence, 12(3), 2018,187–226.
  28. [28] A. Elnagar, Prediction of moving objects in dynamicenvironments using Kalman filters, Pro. IEEE InternationalSymposium on Computational Intelligence in Robotics andAutomation, Banff, AB, 2001, 414–419.
  29. [29] F. Farahi and H.S. Yazdi, Probabilistic Kalman filter for movingobject tracking, Signal Processing: Image Communication, 82,2020, 115751. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0923596519302395
  30. [30] M. Ribeiro and I. Ribeiro, Kalman and extended Kalman filters:Concept, derivation and properties, Institute for Systems andRobotics, 43(46), Feb 2004, 3736–3741.

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