The problem of finding a lost target in a noisy environment by a group of flying vehicles is studied in this article. The developed cooperative search algorithm that is decentrally applied on the flying vehicles is a combination of searching guidance and neighborhood laws. The searching guidance law generates an acceleration command to direct each flying vehicle to the position of the lost target. The command is generated based on the information gathered by those flying vehicles that are categorized as neighbors by the neighborhood law. The neighborhood law specifies the sharing network between the flying vehicles for intelligent cooperation. Various neighborhood laws are introduced for tuning the search exploration and exploitation, which influence the performance of the cooperative search algorithm. To evaluate this performance, two approaches are considered. The analytical approach shows that the search process is stable and convergent. In the second approach, numerical simulations demonstrate that properly selecting the neighborhood law significantly enhances the performance of the search.
ShimaTRasmussenS. UAV cooperative decision and control, Philadelphia, PA: Society for Industrial and Applied Mathematics, 2009.
2.
BeniGWangJ. Swarm intelligence in cellular robotic systems. In: Robots and biological systems: towards a new bionics?, NATO ASI Series. Berlin, Heidelberg, Germany: Springer, 1993, pp. 703–712.
3.
FloreanoDMattiussiC. Bio-inspired artificial intelligence: theories, methods, and technologies, Cambridge, MA: The MIT Press, 2008.
4.
SahinEWinfieldA. Special issue on swarm robotics. Swarm Intell2008; 2(2–4): 69–72.
5.
Sahin E. Swarm robotics: from sources of inspiration to domains of application. In: Sahin E and Spears W (eds) Swarm robotics: state-of-the-art survey, 3342, lecture notes in computer science, 2005, pp.10–20. Berlin, Heidelberg, Germany: Springer-Verlag.
6.
BayindirLSahinE. A review of studies in swarm robotics. Turk J Electr Eng2007; 15(2): 115–147.
7.
Martinoli A. Swarm intelligence in autonomous collective robotics: from tools to the analysis and synthesis of distributed control strategies. PhD Thesis, EPFL, Lausanne, Switzerland, 1999.
8.
MartinoliAEastonKAgassounonW. Modeling swarm robotic systems: a case study in collaborative distributed manipulation. Int J Robot Res2004; 23: 415–435.
9.
Othman W, Amavasai BP, McKibbin SP, et al. An analysis of collective movement models for robotic swarms. In: EUROCON the international conference on “computer as a tool”, Warsaw, Poland, 9–12 September 2007, pp.2373–2380.
10.
WeiXing F, KeJun W, XiuFen Y, et al. Novel algorithms for coordination of underwater swarm robotics. In: IEEE international conference on mechatronics and automation, China, 25–28 June 2006, pp.654–659.
11.
Bahceci E and SahinE. Evolving aggregation behaviour for swarm robotic systems: a systematic case study. In: Proceeding of swarm intelligence symposium SIS, Pasadena, California, 8–10 June 2005, pp.333–340.
12.
TurgutAÇelikkanatHGökçeF. Self-organized flocking in mobile robot swarms. Swarm Intell2008; 2: 97–120.
13.
GokceFSahinE. The pros and cons of flocking in the long-range “migration” of mobile robot swarms. Theor Comp Sci2010; 411(21): 2140–2154.
14.
PughJMartinoliASmallscale robot formation movement using a simple on-board relative positioning system. In: KhatibOKumarVRusD (eds). Experimental robotics, Springer tracts in advanced robotics, 20082008. STAR 39, pp.297–306. Berlin, Heidelberg, Germany: Springer-Verlag.
15.
Tanner HG and Kumar A. Formation stabilization of multiple agents using decentralized navigation functions. In: Proceedings of robotics: science and systems, Cambridge, MA, USA, 8–11 June 2005, pp.49–56.
16.
Pearce JL, Rybski PE, Stoeter SA, et al. Dispersion behaviors for a team of multiple miniature robots. In: Proceedings of the 2003 IEEE international conference on robotics & automation, Taipie, Taiwan, 14–19 September 2003, pp. 1158–1163.
17.
LudwigLGiniM. Robotic swarm dispersion using wireless intensity signals. In: Distributed autonomous robotic systems, Berlin, Germany: Springer-Verlag, 2006, pp. 135–144.
18.
LiuYPassinoKM. Stable social foraging swarms in noisy environment. IEEE Trans Automat Control2004; 49(1): 30–44.
19.
HayesATMartinoliAGoodmanRM. Swarm robotic odor localization: off-line optimization and validation with real robots. In: Robotica, Cambridge, MA: Cambridge University Press, 2003, pp. 427–441. DOI: 10.1017/S0263574703004946.
20.
Shell DA and Mataric MJ. On foraging strategies for large-scale multi-robot systems. In: Proceedings of the 2006 IEEE/RSJ international conference on intelligent robots and systems, Beijing, China, October 9–15, 2006, pp.2717–2723.
PrithvirajD. Multi-robot task allocation for performing cooperative foraging tasks in an initially unknown environment. In: Innovations in defence support systems-2, studies in computational intelligence, vol 338, Berlin, Germany: Springer-Verlag, 2011, pp. 5–20.
23.
ParrishJHamnerW. Animal groups in three dimensions, Cambridge, MA: Cambridge University Press, 1997.
24.
MogilnerAEdelstein-KeshetL. A nonlocal model for a swarm. J Math Biol1999; 38: 534–570.
25.
LeeWBangHLeeghimH. A cooperative guidance law for target estimation by multiple unmanned aerial vehicles. Proc Inst MechE, Part G: J Aerospace Eng2011; 225(12): 1322–1335.
26.
StachuraMFrewEW. Cooperative target localization with a communication-aware unmanned aircraft system. J Guidance Control Dyn2011; 34(5): 1352–1362.
27.
BelbachirAIngrandFLacroixS. A cooperative architecture for target localization using multiple AUVs. Intell Service Robot2012; 5(2): 119–132.
28.
JinYLiaoYMinaiAA. Balancing search and target response in cooperative unmanned aerial vehicle (UAV) teams. IEEE Trans Syst Man Cybern Part: B Cybern2005; 36: 571–587.
29.
ManatharaJGSujitPBBeardRW. Multiple UAV coalitions for a search and prosecute mission. J Intell Robot Syst2011; 62: 125–158.
30.
PackDJDeLimaPToussaintGJ. Cooperative control of UAVs for localization of intermittently emitting mobile targets. IEEE Trans Syst Man Cybern Part:B Cybern2009; 39(4): 959–970.
31.
ZipfelPH. Modeling and simulation of aerospace vehicle dynamicsUSA: AIAA, 2007, 2nd edn. AIAA Education Series.
32.
OgataK. Modern control engineering, 5th edn. London, UK: Prentice Hall, 2009.
33.
KreyszigE. Advanced engineering mathematics, 10th edn. New York: John Wiley, 2011.
34.
SegalSBen-AsherJZWeissH. Derivation of formation-flight guidance laws for unmanned air vehicles. AIAA J Guidance Control Dyn2005; 28: 733–742.
