Hostname: page-component-6bf8c574d5-2jptb Total loading time: 0 Render date: 2025-02-23T04:08:42.738Z Has data issue: false hasContentIssue false
  • English
  • Français

Obstacle avoidance in V-shape formation flight of multiple fixed-wing UAVs using variable repulsive circles

Published online by Cambridge University Press:  23 October 2020

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]  and
S. Etemadi Haghighi Open the ORCID record for S. Etemadi Haghighi [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
*
[email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

A formation control and obstacle avoidance algorithm has been introduced in this paper for the V-shape formation flight of fixed-wing UAVs (Unmanned Aerial Vehicles) using the potential functions method. An innovative vector approach has been suggested to fix the conventional challenge in employing the artificial potential field (APF) approach (the creation of local minimums). A method called variable repulsive circles (VRC) has been then presented aimed at designing proper flight paths tailored with functional limitations of fixed-wing UAVs in facing obstacles. Finally, the efficiency of the designed algorithm has been examined and evaluated for different flight scenarios.

Keywords

fixed-wing UAVs formation flightvariable V-shape formationstraight-line formationVRCs obstacle avoidance
Type
Research Article
Information
The Aeronautical Journal , Volume 124 , Issue 1282 , December 2020 , pp. 1979 - 2000
Copyright
© The Author(s), 2020. Published by Cambridge University Press on behalf of Royal Aeronautical Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Anthony, M. and Kar-Han, T. High precision formation control of mobile robots using virtual structures, Autonom Robots 1997; 4, (4), pp 387–403. https://doi.org/10.1023/A:1008814708459 Google Scholar
Pantelimon, G., Tepe, K., Carriveau, R. and Ahmed, S. Survey of multi-agent communication strategies for information exchange and mission control of drone deployments, J Intell Robot Syst 2019; 95, (3–4), pp 779–788. https://doi.org/10.1007/s10846-018-0812-x CrossRefGoogle Scholar
Liu, Y. and Bucknall, R. A survey of formation control and motion planning of multiple unmanned vehicles, Robotica 2013; 36, (7), pp 1019–1047. https://doi.org/10.1017/S0263574718000218 CrossRefGoogle Scholar
Issa Bayadir, A. and Rashid Abdulmuttalib, T. A survey of multi-mobile robot formation control, Int J Comput Appl 2019; 181, (48), pp 12–16. https://doi.org/10.5120/ijca2019918651 Google Scholar
Ai, X.L., Yu, J.Q., Chen, Y.B., Chen, F.Z. and Shen, Y.C. Optimal formation control with limited communication for multi-unmanned aerial vehicle in an obstacle-laden environment, Proc IMechE Part G: J Aerospace Eng 2016; 23, (6), pp 979–997. https://doi.org/10.1177/0954410016646599 Google Scholar
Do, K.D. and Pan, J. Nonlinear formation control of unicycle-type mobile robots, Robot Autonom Syst 2007; 55, (3), pp 191–204. https://doi.org/10.1016/j.robot.2006.09.001 CrossRefGoogle Scholar
Consolini, L., Morbidi, F., Prattichizzo, D. and Tosques, M. Leader–follower formation control of nonholonomic mobile robots with input constraints, Automatica 2008; 44, (5), pp 1343–1349 https://doi.org/10.1016/j.automatica.2007.09.019 CrossRefGoogle Scholar
Lee, G. and Chwa, D. Decentralized behavior-based formation control of multiple robots considering obstacle avoidance, Intel Serv Robot 2017; 11, (1), pp 127–138. https://doi.org/10.1007/s11370-017-0240-y CrossRefGoogle Scholar
Peng, Z., Wang, D., Chen, Z., Hu, X. and Lan, W. Adaptive dynamic surface control for formations of autonomous surface vehicles with uncertain dynamics, IEEE Trans Cont Syst Technol 2013; 21, (2), pp 513–520. https://doi.org/10.1109/TCST.2011.2181513 CrossRefGoogle 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, (5), pp 1008–1018. 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, IEEE-International Conference on Advances in Engineering, Science and Management (ICAESM-2012), Nagapattinam, Tamil Nadu, India, pp. 500–505. https://ieeexplore.ieee.org/document/6216054 Google Scholar
Dang, A.D., La, H.M., Nguyen, T. and Horn, J. Distributed formation control for autonomous robots in dynamic environments. Published in ArXiv, 2017. Google Scholar
Khatib, O. Real-time obstacle avoidance for manipulators and mobile robots, Int J Robot Res 1986; 5, (1), pp 90–99. https://doi.org/10.1177/027836498600500106 CrossRefGoogle Scholar
Reif, J.H. and Wang, H. Social potential fields: a distributed behavioral control for autonomous robots, Robot Autonom Syst 1999; 27, (13), pp 171–194. https://doi.org/10.1016/S0921-8890(99)00004-4 CrossRefGoogle Scholar
Dong, H.K., Hua, W. and Seiichi, S. Decentralized control of autonomous swarm systems using artificial potential functions: analytical design guidelines, J Intell Robot Syst 2006; 45, (4), pp 369–394. https://doi.org/10.1007/s10846-006-9050-8 Google Scholar
Chen, Y., Yu, J., Su, X. and Luo, G. Path planning for multi-UAV formation, J Intell Robot Syst 2015; 77 (1), pp 229–246. https://doi.org/10.1007/s10846-014-0077-y CrossRefGoogle Scholar
Etemadi, S., Vatankhah, R., Alasty, A., Vossoughi, G.R. and Boroushakic, M. Leader connectivity management and flocking velocity optimization using the particle swarm optimization method, Scientia Iranica, Trans B Mech Eng 2012; 19, (5), pp 1251–1257. https://doi.org/10.1016/j.scient.2012.06.029 CrossRefGoogle Scholar
Min, H., Sun, F. and Niu, F. Decentralized UAV formation tracking flight control using gyroscopic force, Proceedings of the International Conference on Computational Intelligence for Measurement Systems and Applications, 2009, pp 91–96. https://doi.org/10.1109/CIMSA.2009.5069925 CrossRefGoogle Scholar
Etemadi, S., Alasty, A. and Vossoughi, G.R. Stability analysis of robotic swarm with limited field of view, Proceedings of IMECE 2007, Seattle, Washington, USA, 2007, pp 175–184. https://doi.org/10.1115/IMECE2007-42062 CrossRefGoogle Scholar
Etemadi, S., Alasty, A. and Vossoughi, G.R. Flocking coordination using active leader and local information, Asian J Cont 2011; 13, (6), pp 797–808. https://doi.org/10.1002/asjc.200 CrossRefGoogle Scholar
Hengster-Movric, K., Bogdan, S. and Draganjac, L. Multi-agent formation control based on bell-shaped potential functions, J Intell Robot Syst 2010; 58, (2), pp 165–189. https://doi.org/10.1007/s10846-009-9361-7 CrossRefGoogle Scholar
Gazi, V., Fidan, B., Hanay, Y.S., Koksal, M.I. Aggregation, foraging, and formation control of swarms with non-holonomic agents using potential functions and sliding mode techniques, Turk J Electr Eng Comput Sci 2007; 15, (2), pp 149–168. https://journals.tubitak.gov.tr/elektrik/abstract.htm?id Google Scholar
Harder, S.A. and Lauderbaugh, L.K. Formation specification for control of active agents using artificial potential fields, J Intell Robot Syst, 2019; 95, (2), pp 279–290. https://doi.org/10.1007/s10846-018-0912-7 CrossRefGoogle Scholar
Gazi, V. and Passino, K.M. Swarm Stability and Optimization, Springer Science, 2011.Google Scholar
Slotine, J.E. and Li, W. Applied Nonlinear Control. Prentice-Hall, 1991.Google Scholar
Rimon, E. and Koditschek, D.E. Exact robot navigation using artificial potential function, IEEE Trans Robot Automat 1992; 8, (5), pp 501–518. https://doi.org/10.1109/70.163777 CrossRefGoogle Scholar
Yao, J., Ordonez, R. and Gazi, V. Swarm tracking using artificial potentials and sliding mode control, J Dyn Syst Meas Cont 2007; 129, (5), pp 749–754. https://doi.org/10.1115/1.2764511 CrossRefGoogle Scholar
Albaker, B.M. and Rahim, N.A. A survey of collision avoidance approaches for unmanned aerial vehicles, International Conference in Technical Postgraduates (TECHPOS), 2009, pp 1–7. https://doi.org/10.1109/TECHPOS.2009.5412074 CrossRefGoogle Scholar
Bilimoria, K., Sridhar, B. and Chatterji, G. Effects of conflict resolution maneuvers and traffic density of free flight, Proceeding 1996 AIAA Guidance, Navigation, and Control Conference, San Diego, CA, 1996. https://doi.org/10.2514/6.1996-3767 CrossRefGoogle Scholar
Federal Aviation Administration, Precision Runway Monitor Demonstration Report. Document DOT/FAA/RD-91/5, February, 1991.Google Scholar
Pham, H., Smolka, S.A., Stoller, S.D., Phan, D. and Yang, J. A survey on unmanned aerial vehicle collision avoidance systems. . Submitted on 31 Aug 2015. .Google Scholar
Sasongko, R.A., Rawikara, S.S. and Tampubolon, H.J. UAV obstacle avoidance algorithm based on ellipsoid geometry, J Intell Robot Syst 2017; 88, (2–4), pp 567–581. https://doi.org/10.1007/s10846-017-0543-4 CrossRefGoogle Scholar
Fiorini, P. and Shiller, Z. Motion planning in dynamic environments using velocity obstacle, Int J Robot Res 1998; 17, (7), pp 760–772. https://doi.org/10.1177/027836499801700706 CrossRefGoogle Scholar
Jianhua, W. and Robert, W. Dynamic obstacle avoidance for an omnidirectional mobile robot, J Robot 2010; 2010, Article ID 901365, 14 pages. http://dx.doi.org/10.1155/2010/901365.Google Scholar
Park, J.W., Oh, H.D. and Tahk, M.J. UAV collision avoidance based on geometric approach, SICE Annual Conference, 2008, pp 2122–2126. https://doi.org/10.1109/SICE.2008.4655013 CrossRefGoogle Scholar
Strobel, A. and Schwarzbach, M. Cooperative sense and avoid: implementation in simulation and real world for small unmanned aerial vehicles, International Conference on Unmanned Aircraft Systems (ICUAS), 2014, pp 1253–1258. https://doi.org/10.1109/ICUAS.2014.6842382 CrossRefGoogle Scholar
Phuong, D.H.N., Recchiuto, C.T. and Sgorbissa, A. Real-time path generation and obstacle avoidance for multirotors: a novel approach, J Intell Robot Syst Arch 2018; 89, (1–2), pp 27–49. https://doi.org/10.1007/s10846-017-0478-9 Google Scholar
Srikanthakumar, S., Liu, C. and Chen, W.H. Optimization-based safety analysis of obstacle avoidance system for unmanned aerial vehicles, J Intell Robot Syst 2012; 65, (1–4), pp 219–231. https://doi.org/10.1007/s10846-011-9586-0 CrossRefGoogle Scholar
Srikanthakumar, S. and Chen, W.H. Worst-case analysis of moving obstacle avoidance systems for unmanned vehicles, Robotica 2015; 33, (4), pp 807–827. https://doi.org/10.1017/S0263574714000642 CrossRefGoogle 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 Sys Meas Cont 2014; 137, (1), pp 014503, 10 pages. https://doi.org/10.1115/1.4028034 CrossRefGoogle Scholar
Anderson, J.D. Jr. Aircraft Performance and Design. Tata McGraw-Hill Edition, 2010.Google Scholar