1. bookVolume 16 (2022): Issue 3 (September 2022)
Journal Details
Format
Journal
eISSN
2300-5319
First Published
22 Jan 2014
Publication timeframe
4 times per year
Languages
English
access type Open Access

Design and Analysis of a Novel Concept-Based Unmanned Aerial Vehicle with Ground Traversing Capability

Published Online: 16 May 2022
Volume & Issue: Volume 16 (2022) - Issue 3 (September 2022)
Page range: 169 - 179
Received: 26 Nov 2021
Accepted: 01 Mar 2022
Journal Details
Format
Journal
eISSN
2300-5319
First Published
22 Jan 2014
Publication timeframe
4 times per year
Languages
English
Abstract

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.

Keywords

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):174–190.10.1016/j.biosystemseng.2010.11.010 Search in Google Scholar

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. Search in Google Scholar

3. Wang Z, McDonald ST. Convex relaxation for optimal rendezvous of unmanned aerial and ground vehicles, Aero Sci and Tech. 2020;99:1–19. Search in Google Scholar

4. Glida HE, Abdou L, Chelihi A, Sentouh C. Optimal model-free backstepping control for a quadrotor helicopter. Nonlin Dyna. 2020;100(4):3449–3468.10.1007/s11071-020-05671-x Search in Google Scholar

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;8:805–815.10.1007/s40435-019-00599-6 Search in Google Scholar

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):619–635.10.1007/s40435-020-00666-3 Search in Google Scholar

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):383–395.10.1007/s41870-020-00436-6 Search in Google Scholar

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. Search in Google Scholar

9. Heidari H, Saska M. Trajectory Planning of Quadrotor Systems for Various Objective Functions. Robo. 2021;39(1):137–152.10.1017/S0263574720000247 Search in Google Scholar

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. Search in Google Scholar

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):483–502.10.1017/S0263574720000521 Search in Google Scholar

12. Xu H, Jiang S, Zhang A. Path Planning for Unmanned Aerial Vehicle Using a Mix-Strategy-Based Gravitational Search Algorithm. IEEE Access, 2021;9:57033–57045.10.1109/ACCESS.2021.3072796 Search in Google Scholar

13. Zhang X, Duan H. An improved constrained differential evolution algorithm for unmanned aerial vehicle global route planning. Appl Soft Comp. 2015;26:270–284.10.1016/j.asoc.2014.09.046 Search in Google Scholar

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):132–141.10.1109/TII.2012.2198665 Search in Google Scholar

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):1712–1718.10.1016/j.asoc.2007.10.011 Search in Google Scholar

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):442–449.10.1016/j.ast.2009.07.002 Search in Google Scholar

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):1760008–1760037.10.1142/S0218213017600089 Search in Google Scholar

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;238:111–125.10.1016/j.ins.2013.02.022 Search in Google Scholar

19. Cui Z, Wang Y. UAV Path Planning Based on Multi-Layer Reinforcement Learning Technique. IEEE Access. 2021;9:59486–59497.10.1109/ACCESS.2021.3073704 Search in Google Scholar

20. Yao M, Zhao M. Unmanned aerial vehicle dynamic path planning in an uncertain environment. Robo. 2015;33(3):611–621.10.1017/S0263574714000514 Search in Google Scholar

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