This article focuses on the navigational control of underwater mobile robot. Differential evolution approach has been used to navigate the underwater robot from source to destination while avoiding various types of obstacles. Differential evolution algorithm has been employed to find out the robot’s global best pose among a set of possible solutions based on the fitness value with respect to the current sensory data about obstacles and target. Such evolutionary computation scheme can provide desirable convergence, diversity and also robustness depending on proper selection and adaptive tuning of parameters. Self-learning ability of the parameters in the path planning algorithm is crucial to deal with nonlinearities and ambiguities of hydrodynamics as created by high-frequency oscillations during underwater motion. A sequence of intermediate positions chosen by proposed dynamic differential evolution algorithm between start and goal points can be defined as a near-optimum path for underwater robot. During navigation of the robot, the path smoothness and clearance from obstacles and computational time are also considered for performance evaluation of implemented algorithm. The feasibility of the proposed underwater motion planning approach has been authenticated through the simulation and experimental results.
SaravanakumarSAsokanT.Multipoint potential field method for path planning of autonomous underwater vehicles in 3D space. Intel Serv Robotics2013; 6: 211–224.
2.
NaeemWSuttonRAhmadSM. A review of guidance laws applicable to unmanned underwater vehicles. J Navigation2003; 56(01): 15–29.
3.
YangLCaoZZhouC. Formation control and switching for multiple robots in uncertain environments. Int J Robot Autom2010; 25(3): 240–249.
4.
BurgardWDerrAFoxD. Integrating global position estimation and position tracking for mobile robots: the dynamic Markov localization approach. In: IEEE/RSJ international conference on intelligent robots and systems (IROS),Victoria, BC, Canada, 13–17 October 1998, vol. 2, pp.730–735. New York: IEEE.
5.
ThrunSFoxDBurgardW. Robust Monte Carlo localization for mobile robots. Artif Int2001; 128: 99–141.
6.
MohantaJCParhiDRPatelSK.Path planning strategy for autonomous mobile robot navigation using Petri-GA optimisation. Comput Elect Eng2011; 37: 1058–1070.
7.
PaulKBCliffR.Modified differential evolution: a greedy random strategy for genetic recombination. Omega2005; 33: 255–265.
8.
SarkerRAbbassHA.Differential evolution for solving multi-objective optimization problems. Asia Pac J Oper Res2004; 21(2): 225–240.
9.
CiviciogluPBesdokE.A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms. Artif Intell Rev2013; 39(4): 315–346.
10.
KachitvichyanukulV.Comparison of three evolutionary algorithms: GA, PSO, and DE. Ind Eng Manag Syst2012; 11(3): 215–223.
11.
CoelhoLSNedjahNMourelleLM.Differential evolution approach using chaotic sequences applied to planning of mobile robot in a static environment with obstacles. Stud Comp Intell2007; 50: 3–22.
12.
WangXXuG.Robot path planning based on chaos concise differential evolution and RFNN control. Open Autom Contr Syst J2014; 6: 69–76.
13.
ChakrabortyJKonarAChakrabortyUK. Distributed cooperative multi-robot path planning using differential evolution. In: Evolutionary IEEE World Congress on computational intelligence, Hong Kong, 1–6 June 2008, pp.718–725. New York: IEEE.
14.
MoHMengL.Robot path planning based on differential evolution in static environment. Int J Digit Content Technol Appl2012; 6(20): 122–129.
15.
BashiriMVatankhahHGhidarySS.Hybrid adaptive differential evolution for mobile robot localization. Intell Serv Robot2012; 5: 99–107.
16.
NikolosIKTsourveloudisNC.Path planning for cooperating unmanned vehicles over 3-D terrain. In: FilipeJCettoJAFerrierJ-L (eds) Informatics in control automation and robotics: lecture notes in electrical engineering, vol. 24. Berlin, Heidelberg: Springer, 2009, pp.153–168.
17.
ZhangXDuanH.An improved constrained differential evolution algorithm for unmanned aerial vehicle global route planning. Appl Soft Comput2015; 26: 270–284.
18.
ZamudaASosaJDH. Differential evolution and underwater glider path planning applied to the short-term opportunistic sampling of dynamic mesoscale ocean structures. Appl Soft Comput2014; 24: 95–108.
19.
GonzalezCBlancoDMorenoL.Optimum robot manipulator path generation using differential evolution. In: IEEE Congress on evolutionary computation (CEC), Trondheim, 18–21 May 2009, pp.3322–3329. New York: IEEE.
20.
FossenTI.Marine control systems: guidance, navigation and control of ships, rigs and underwater vehicles. Trondheim: Marine Cybernetics, 2002.
21.
StornRPriceKV.Differential evolution: a simple and efficient heuristic for global optimization over continuous spaces. J Global Opt1997; 11(4): 341–359.
GämperleRMüllerSDKoumoutsakosP.A parameter study for differential evolution. In: GrmelaAMastorakisNE (eds) Advances in intelligent systems, fuzzy systems, evolutionary computation. Interlaken: WSEAS Press, 2002, pp.293–298.
27.
RonkkonenJKukkonenSPriceKV.Real parameter optimization with differential evolution. In: IEEE Congress on evolutionary computation (CEC), vol. 1, Edinburgh, 5 September 2005, vol. 1, pp.506–513. New York: IEEE.
28.
KaeloPAliMM.A numerical study of some modified differential evolution algorithms. Eur J Oper Res2006; 169: 1176–1184.
29.
KarabogaDAkayB.A comparative study of artificial bee colony algorithm. Appl Math Comput2009; 214(12): 108–132.
30.
ZaharieD.Influence of crossover on the behaviour of differential evolution algorithms. Appl Soft Comput2009; 9(3): 1126–1138.
31.
BrestJGreinerSBoskoviˇB. Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE T Evolut Comput2006; 10(6): 646–657.
32.
FanHYLampinenJ.A trigonometric mutation operator to differential evolution. J Global Optim2003; 27(1): 105–129.
BrestJGreinerSBoskoviˇB. Performance comparison of self-adaptive and adaptive differential evolution algorithms. Soft Comput2007; 11: 617–629.
35.
DasSAbrahamAKonarA.Automatic clustering using an improved differential evolution algorithm. IEEE T Syst Man Cy A2008; 38(1): 218–237.
36.
ZhangJSandersonAC.JADE: adaptive differential evolution with optional external archive. IEEE T Evolut Comput2009; 13(5): 945–958.
37.
WangXZhaoS.Differential evolution algorithm with self-adaptive population resizing mechanism. Math Probl Eng2013; 2013: 419372 (14 pp.).
38.
QinAKHuangVLSuganthanPN.Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE T Evolut Comput2009; 13(2): 398–417.
39.
DasSAbrahamAChakrabortyUK. Differential evolution using a neighbourhood-based mutation operator. IEEE T Evolut Comput2009; 13(3): 526–553.
40.
AliMPantMSinghVP.An improved differential evolution algorithm for real parameter optimization problems. Int J Recent Trends Eng2009; 1(5): 63–65.
41.
NeriFTirronenV.Recent advances in differential evolution: a survey and experimental analysis. Artif Intell Rev2010; 33: 61–106.
42.
WangYCaiZZhangQ.Differential evolution with composite trial vector generation strategies and control parameters. IEEE T Evolut Comput2011; 15(1): 55–66.
43.
ZouDLiuHGaoL. A novel modified differential evolution algorithm for constrained optimization problems. Comput Math Appl2011; 61: 1608–1623.
44.
PanQKSuganthanPNWangL. A differential evolution algorithm with self-adapting strategy and control parameters. Comput Oper Res2011; 38: 394–408.
45.
MallipeddiRSuganthanPNPanQK. Differential evolution algorithm with ensemble of parameters and mutation strategies. Appl Soft Comput2011; 11(2): 1679–1696.
46.
GongWCaiZLingCX. Enhanced differential evolution with adaptive strategies for numerical optimization. IEEE T Syst Man Cy C2011; 41(2): 397–413.
47.
MandalADasAKMukherjeeP. Modified differential evolution with local search algorithm for real world optimization. In: IEEE Congress on evolutionary computation (CEC), New Orleans, LA, 5–8 June 2011, pp.1565–1572. New York: IEEE.
48.
NomanNIbaH.Accelerating differential evolution using an adaptive local search. IEEE T Evolut Comput2008; 12(1): 107–125.
49.
JiaDZhengGKhanMK.An effective memetic differential evolution algorithm based on chaotic local search. Inform Sciences2011; 181: 3175–3187.
50.
DasSSuganthanPN.Differential evolution: a survey of the state-of-the-art. IEEE T Evolut Comput2011; 15(1): 4–31.
51.
KumarPPantMSinghVP.Differential evolution with interpolation based mutation operators for engineering design optimization. Adv Mech Eng Appl2012; 2(3): 221–231.
52.
ShamekhiA.An improved differential evolution optimization algorithm. Int J Res Rev Appl Sci2013; 15(2): 132–145.
53.
ChengJZhangGNeriF.Enhancing distributed differential evolution with multicultural migration for global numerical optimization. Inform Sciences2013; 247: 72–93.
54.
VahdatARNourAshrafoddinNGhidarySS.Mobile robot global localization using differential evolution and particle swarm optimization. In: IEEE Congress on evolutionary computation (CEC), Singapore, 25–28 September 2007, pp.1527–1534. New York: IEEE.
55.
GuangleiZHemingJ. 3D path planning of AUV based on improved ant colony optimization. In: 32nd Chinese control conference (CCC), Xi’an, China, 26–28 July 2013, pp.5017–5022. New York: IEEE.
56.
JiangLZhuD.Three-dimensional path planning for AUV based on fuzzy control. In: SunZDengZ (eds) Chinese intelligent automation conference: lecture notes in electrical engineering, vol. 254. Berlin, Heidelberg: Springer, 2013, pp.31–40.
