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Design and analysis of a novel concept-based unmanned aerial vehicle with ground traversing capability

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Unmanned aerial vehicle (UAV) is a typical aircraft that is operated remotely by a human operator or autonomously by an on-board microcontroller. The UAV typically carries offensive ordnance, target designators, sensors or electronic transmitters designed for one or more applications. Such application can be in the field of defence surveillance, border patrol, search, bomb disposals, logistics and so forth. These UAVs are also being used in some other areas, such as medical purposes including for medicine delivery, rescue operations, agricultural applications and so on. However, these UAVs can only fly in the sky, and they cannot travel on the ground for other applications. Therefore, in this paper, we design and present the novel concept-based UAV, which can also travel on the ground and rough terrain as an unmanned ground vehicle (UGV). This means that according to our requirement, we can use this as a quadcopter and caterpillar wheel–based UGV using a single remote control unit. Further, the current study also briefly discusses the two-dimensional (2D) and three-dimensional (3D) SolidWorks models of the novel concept-based combined vehicle (UAV + UGV), together with a physical model of a combined vehicle (UAV + UGV) and its various components. Moreover, the kinematic analysis of a combined vehicle (UAV + UGV) has been studied, and the motion controlling kinematic equations have been derived. Then, the real-time aerial and ground motions and orientations and control-based experimental results of a combined vehicle (UAV + UGV) are presented to demonstrate the robustness and effectiveness of the proposed vehicle.
Rocznik
Strony
169--179
Opis fizyczny
Bibliogr. 20 poz., rys., tab., wykr.
Twórcy
  • School of Mechanical Engineering, Kalinga Institute of Industrial Technology Deemed to be University, An Institute of Eminence, Patia, Campus-8, Bhubaneswar, 751024, Odisha, India
  • School of Mechanical Engineering, Kalinga Institute of Industrial Technology Deemed to be University, An Institute of Eminence, Patia, Campus-8, Bhubaneswar, 751024, Odisha, India
autor
  • School of Mechanical Engineering, Kalinga Institute of Industrial Technology Deemed to be University, An Institute of Eminence, Patia, Campus-8, Bhubaneswar, 751024, Odisha, India
  • School of Mechanical Engineering, Kalinga Institute of Industrial Technology Deemed to be University, An Institute of Eminence, Patia, Campus-8, Bhubaneswar, 751024, Odisha, India
  • School of Mechanical Engineering, Kalinga Institute of Industrial Technology Deemed to be University, An Institute of Eminence, Patia, Campus-8, Bhubaneswar, 751024, Odisha, India
  • School of Mechanical Engineering, Kalinga Institute of Industrial Technology Deemed to be University, An Institute of Eminence, Patia, Campus-8, Bhubaneswar, 751024, Odisha, India
  • School of Mechanical Engineering, Kalinga Institute of Industrial Technology Deemed to be University, An Institute of Eminence, Patia, Campus-8, Bhubaneswar, 751024, Odisha, India
Bibliografia
  • 1. Xiang H, Tian L. Development of a low-cost agricultural remote sensing system based on an autonomous unmanned aerial vehicle (UAV). Biosystems Engg. 2011;108(2):174–190.
  • 2. Tahar KN, Ahmad A. A simulation study on the capabilities of rotor wing unmanned aerial vehicle in aerial terrain mapping. Int J of Phy Sci. 2012;7(8):1300–1306.
  • 3. Wang Z, McDonald ST. Convex relaxation for optimal rendezvous of unmanned aerial and ground vehicles, Aero Sci and Tech. 2020;99:1–19.
  • 4. Glida HE, Abdou L, Chelihi A, Sentouh C. Optimal model-free backstepping control for a quadrotor helicopter. Nonlin Dyna. 2020;100(4):3449–3468.
  • 5. Labbadi M, Cherkaoui M. Novel robust super twisting integral sliding mode controller for a quadrotor under external disturbances. Int J of Dyna and Cont. 2020;8:805–815.
  • 6. Hassani H, Mansouri A, Ahaitouf A. Robust autonomous flight for quadrotor UAV based on adaptive nonsingular fast terminal sliding mode control. Int J of Dyna and Cont. 2021;9(2):619–635.
  • 7. Selma B, Chouraqui S, Abouaïssa H. Optimal trajectory tracking control of unmanned aerial vehicle using ANFIS-IPSO system. Int J of Info Techn. 2020;12(2):383–395.
  • 8. Elijah T, Jamisola RS, Tjiparuro Z, Namoshe M (2020). A review on control and maneuvering of cooperative fixed-wing drones. Int J of Dyna and Cont. 202;9(3):1332–1349.
  • 9. Heidari H, Saska M. Trajectory Planning of Quadrotor Systems for Various Objective Functions. Robo. 2021;39(1):137–152.
  • 10. Abdalla M, Al-Baradie S. Real time optimal tuning of quadcopter attitude controller using particle swarm optimization, J of Eng and Techno Sci. 2020;52(5):745–764.
  • 11. Pinto MF, Honório LM, Marcato AL, Dantas MA, Melo AG, Capretz M, Urdiales C. ARCog: An Aerial Robotics Cognitive Architecture. Robo. 2021;39(3):483–502.
  • 12. Xu H, Jiang S, Zhang A. Path Planning for Unmanned Aerial Vehicle Using a Mix-Strategy-Based Gravitational Search Algorithm. IEEE Access, 2021;9:57033–57045.
  • 13. Zhang X, Duan H. An improved constrained differential evolution algorithm for unmanned aerial vehicle global route planning. Appl Soft Comp. 2015;26:270–284.
  • 14. Roberge V, Tarbouchi M, Labonté G. Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning. IEEE Trans on Indu Informat. 2012;9(1):132–141.
  • 15. Mou C, Qing-Xian W, Chang-Sheng J. A modified ant optimization algorithm for path planning of UCAV. Appl Soft Comp. 2008;8(4):1712–1718.
  • 16. Duan H, Liu S, Wu J. Novel intelligent water drops optimization approach to single UCAV smooth trajectory planning. Aero Sci and Tech, 2009;13(8):442–449.
  • 17. Silva Arantes JD, Silva Arantes MD, Motta Toledo CF, Júnior OT, Williams BC. Heuristic and genetic algorithm approaches for UAV path planning under critical situation. Int J on Art Intel Tools. 2017;26(01):1760008–1760037.
  • 18. Besada-Portas E, De La Torre L, Moreno A, Risco-Martin JL. On the performance comparison of multi-objective evolutionary UAV path planners. Info Sci, 2013;238:111–125.
  • 19. Cui Z, Wang Y. UAV Path Planning Based on Multi-Layer Rein-forcement Learning Technique. IEEE Access. 2021;9:59486–59497.
  • 20. Yao M, Zhao M. Unmanned aerial vehicle dynamic path planning in an uncertain environment. Robo. 2015;33(3):611–621.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d246e911-7a4c-417d-8e2d-09468ebdcabe
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