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Real-time formation control and obstacle avoidance algorithm for fixed-wing UAVs

Published online by Cambridge University Press:  23 February 2022

A. Mirzaee Kahagh Open the ORCID record for A. Mirzaee Kahagh [Opens in a new window] ,
F. Pazooki Open the ORCID record for F. Pazooki [Opens in a new window] ,
S. Etemadi Haghighi Open the ORCID record for S. Etemadi Haghighi [Opens in a new window]  and
D. Asadi Open the ORCID record for D. Asadi [Opens in a new window]
A. Mirzaee Kahagh
Affiliation:
Department of Aerospace Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
F. Pazooki
Affiliation:
Department of Aerospace Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
S. Etemadi Haghighi*
Affiliation:
Department of Mechanical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
D. Asadi
Affiliation:
Department of Aerospace Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
*
Corresponding author. Email: [email protected]
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Abstract

This paper proposes a novel real-time formation control and obstacle avoidance algorithm for multiple fixed-wing UAVs. A formation control algorithm is designed by a combination of the virtual structure, leader-follower, and artificial potential fields methods and harnessing the advantages of those approaches. The kinematic and dynamic constraints of fixed-wing UAVs are considered in the path planning. The performance of the proposed algorithm is examined through simulation in Matlab software by applying the translational dynamics of fixed-wing UAVs. Simulations of different complex scenarios demonstrate the effectiveness of the presented formation flight algorithm through generating multiple efficient paths, which are fully consistent with the functional constraints of the UAVs in the presence of obstacles.

Keywords

Formation controlObstacle avoidanceMultiple fixed-wing UAVsArtificial potential fieldsLeader-followerVirtual structure
Type
Research Article
Information
The Aeronautical Journal , Volume 126 , Issue 1306 , December 2022 , pp. 2111 - 2133
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Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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References

Toyota, R. and Namerikawa, T. Formation control of multi-agent system considering obstacle avoidance. In: 2017 56th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE) 19–22 Sept. 2017, pp 446–451.Google Scholar
Lee, G. and Chwa, D. Decentralized behavior-based formation control of multiple robots considering obstacle avoidance. Intell. Serv. Robot., 2018, 11, pp 127–138.Google Scholar
Ren, W. and Beard, R.W. Decentralized scheme for spacecraft formation flying via the virtual structure approach. J. Guid. Control Dyn., 2004, 27, pp 73–82.CrossRefGoogle Scholar
Liu, Y. and Bucknall, R. A survey of formation control and motion planning of multiple unmanned vehicles. Robotica, 2018, 36, pp 1019–1047.CrossRefGoogle Scholar
Dang, A and Horn, J. Formation control of leader-following uavs to track a moving target in a dynamic environment. J. Autom. Control Eng., 2015, 3, pp 1–8.Google Scholar
Achmadi, S., Marjono, and Miswanto, . Analysis multi-agent with precense of the leader. In: AIP Conference Proceedings 2017, p 020015. AIP Publishing LLC.Google Scholar
Oh, K.-K., Park, M.-C. and Ahn, H.-S. A survey of multi-agent formation control. Automatica, 2015, 53, pp 424–440. DOI: 10.1016/j.automatica.2014.10.022.CrossRefGoogle Scholar
Issa, B. and Rashid, A.T. A survey of multi-mobile robots formation control. Int. J. Comput. Appl., 2019, 181, pp 12–16.Google Scholar
Do, K.D. and Pan, J. Nonlinear formation control of unicycle-type mobile robots. Rob. Auton. Syst., 2007, 55, pp 191–204.CrossRefGoogle Scholar
Pantelimon, G., Tepe, K., Carriveau, R., et al. Survey of multi-agent communication strategies for information exchange and mission control of drone deployments. J. Intell. Robot. Syst., 2019, 95, pp 779–788. DOI: 10.1007/s10846-018-0812-x.Google Scholar
Qian, D., Tong, S. and Li, C. Leader-following formation control of multiple robots with uncertainties through sliding mode and nonlinear disturbance observer. ETRI J. 2016, 38, pp 1008–1018. DOI: https://doi.org/10.4218/etrij.16.0116.0048.CrossRefGoogle Scholar
Kowdiki, K.H., Barai, R.K. and Bhattacharya, S. Leader-follower formation control using artificial potential functions: A kinematic approach. In: IEEE-International Conference On Advances In Engineering, Science And Management (ICAESM-2012) 2012, pp 500–505. IEEE.Google Scholar
Dang, A.-D., La, H.M., Nguyen, T., et al. Distributed formation control for autonomous robots in dynamic environments. arXiv preprint arXiv:170502017 2017.Google Scholar
Tang, L., Dian, S., Gu, G., et al. A novel potential field method for obstacle avoidance and path planning of mobile robot. 2010 3rd International Conference on Computer Science and Information Technology 2010; 9: 633–637.CrossRefGoogle Scholar
Olfati-Saber, R. Flocking for multi-agent dynamic systems: Algorithms and theory. IEEE Trans. Automat. Control, 2006, 51, pp 401–420.Google Scholar
Rostami, S.M.H., Sangaiah, A.K., Wang, J., et al. Obstacle avoidance of mobile robots using modified artificial potential field algorithm. EURASIP J. Wirel. Commun. Netw., 2019, 2019, 70. DOI: 10.1186/s13638-019-1396-2.Google Scholar
Sasongko, R.A., Rawikara, S. and Tampubolon, H.J. Uav obstacle avoidance algorithm based on ellipsoid geometry. J. Intell. Robot. Syst., 2017, 88, pp 567–581.CrossRefGoogle Scholar
Han, K., Lee, J. and Kim, Y. Unmanned aerial vehicle swarm control using potential functions and sliding mode control. Proc. Inst. Mech. Eng. G J. Aerosp. Eng., 2008, 222, pp 721–730.Google Scholar
Stastny, T.J., Garcia, G.A. and Keshmiri, S.S. Collision and obstacle avoidance in unmanned aerial systems using morphing potential field navigation and nonlinear model predictive control. J. Dyn. Syst. Meas. Control, 2015, 137, 014503 (10 pages).CrossRefGoogle Scholar
Ai, X.L., Yu, J.Q., Chen, Y.B., et al. Optimal formation control with limited communication for multi-unmanned aerial vehicle in an obstacle-laden environment. Proc. Inst. Mech. Eng. G J. Aerosp. Eng., 2017, 231, pp 979–997.CrossRefGoogle Scholar
Mirzaee Kahagh, A., Pazooki, F. and Etemadi Haghighi, S. Obstacle avoidance in V-shape formation flight of multiple fixed-wing UAVs using variable repulsive circles. Aeronaut. J., 2020, 124, pp 1979–2000. DOI: 10.1017/aer.2020.81.CrossRefGoogle Scholar
Chang, K., Xia, Y. and Huang, K. UAV formation control design with obstacle avoidance in dynamic three-dimensional environment. SpringerPlus, 2016, 5, 1124. DOI: 10.1186/s40064-016-2476-y.CrossRefGoogle ScholarPubMed
Wang, J. and Xin, M. Integrated optimal formation control of multiple unmanned aerial vehicles. IEEE Trans. Control Syst. Technol., 2013, 21, pp 1731–1744.CrossRefGoogle Scholar
Niculescu, M. Lateral track control law for Aerosonde UAV. In: 39th Aerospace Sciences Meeting and Exhibit 2001, p 16.Google Scholar
Roskam, J. Airplane Flight Dynamics and Automatic Flight Controls. DARcorporation, DARcorporation, Lawrence, Kansas, 1998.Google Scholar