The present paper describes a real-time motion-planning approach which lies in the integration of three techniques: fuzzy logic (FL), virtual force field (VFF), and boundary following (BF). The FL algorithm is used for velocity control based on sonar readings. The path-planning algorithm is based on the VFF and BF methods. The proposed navigation system differs from previous works in terms of using different algorithms for planning robot motion. Other improvements concern functional and computational aspects of the design and integration of the modules. The robot shows robust performance in complex situations and local minimum scenarios. Simulation results show the effectiveness of the developed system in various environments with long walls, U-shaped, maze-like, and other types of clutter.
VelagicL.LacevicB.PerunicicB., ‘A 3-level autonomous mobile robot navigation system designed by using reasoning/search approaches.’Robot. Autonom. Syst.54 (12) (2006): 989–1004.
2.
ChatterjeeR.MatsunoF., ‘Use of single side reflex for autonomous navigation of mobile robots in unknown environments.’Robot. Autonom. Syst.35 (2) (2001): 77–96.
3.
ZhangM.PengS.MengQ., ‘Neural network and fuzzy logic techniques based collision avoidance for a mobile robot.’Robotica15 (6) (1997): 627–632.
4.
XuW. L., ‘A virtual target approach for resolving the limit cycle problem in navigation of a fuzzy behaviour-based mobile robot.’Robot. Autonom. Syst.30 (4) (2000): 315–324.
5.
TakahashiO.SchillingR. J., ‘Motion planning in a plane using generalized Voronoi diagrams.’IEEE Trans. Robot. Autom.5 (2) (1989): 143–150.
6.
BorensteinJ.KorenY., ‘The vector field histogram - fast obstacle avoidance for mobile robots.’IEEE Trans. Robot. Autom.7 (6) (1991): 1179–1187.
7.
LumelshyV.SkewisT., ‘Incorporating range sensing in the robot navigation function.’IEEE Trans. Syst. Man Cybern.30 (5) (1990): 1058–1069.
8.
SeneviratneL. D.KoW. S.EarlesS. W. E., ‘Triangulation-based path planning for a mobile robot.’Proc. IMechE, Part C: J. Mechanical Engineering Science211 (5) (1997): 365–371 DOI: 10.1243/0954406971522114.
9.
SantoshK. D.AcharS.JawaharC. V., ‘Autonomous image-based exploration for mobile robot navigation.’ In Proceedings of IEEE International Conference on Robotics and Automation (ICRA), Pasadena, California, USA, 19–23 May 2008, pp. 2717–2722.
10.
TaniJ., ‘Model-based learning for mobile robot navigation from the dynamical systems perspective.’IEEE Trans. Syst. Man Cybern.26 (3) (1996): 421–436.
11.
ElfesA., ‘Sensor-based real world mapping and navigation.’IEEE J. Robot. Autom. RA-3 (3) (1987): 249–265.
12.
ChosetH.WalkerS.EiamsaK.BurdickJ., ‘Sensor-based exploration: incremental construction of the hierarchical generalized Voronoi graph.’Int. J. Robot.19 (2) (2000): 126–148.
13.
LazarusS. B.TsourdosA.SilsonP.WhiteB.ŻbikowskiR., ‘Unmanned aerial vehicle navigation and mapping.’Proc. IMechE, Part G: J. Aerospace Engineering222 (4) (2008): 531–548 DOI: 10.1243/09544100JAERO246.
14.
MaarefH.BarretC., ‘Sensor-based navigation of a mobile robot in an indoor environment.’Robot. Autonom. Syst.38 (1) (2002): 1–18.
15.
MinguezJ.MontanoL., ‘Autonomous sensor-based motion control in unknown, dynamic and troublesome scenarios.’Robot. Autonom. Syst.52 (4) (2005): 290–311.
16.
KamonI.RivlinE., ‘Sensory-based motion planning with global proofs.’IEEE Trans. Robot. Autom.13 (6) (1997): 814–822.
17.
MotlaghO. R. E.TangS. H.IsmailN., ‘Development of a new minimum avoidance system for a behaviour-based mobile robot.’Fuzzy Sets Syst.160 (13) (2009):1929–1946.
18.
WahdeM., ‘A method for behavioural organization for autonomous robots based on evolutionary optimization of utility functions.’Proc. IMechE, Part I: J. Systems Control and Engineering217 (4) (2003): 249–258.
19.
YeC.WangD., ‘A novel behaviour fusion method for the navigation of mobile robots.’J. Intell. Robot. Syst.32 (4) (2001): 361–388.
20.
SerajiH.HowardA., ‘Behaviour-based robot navigation on challenging terrain: a fuzzy logic approach.’IEEE Trans. Robot. Autom.18 (3) (2002): 308–321.
HuangH. P.LeeP. C., ‘A real-time algorithm for obstacle avoidance of autonomous mobile robots.’Robotica10 (3) (1992): 217–227.
23.
WangM.LiuJ. N. K., ‘Fuzzy logic-based real-time robot navigation in unknown environment with dead ends.’Robot. Autonom. Syst.56 (7) (2008): 625–643.
24.
PinF. G.BenderS. R., ‘Adding memory processing behaviour to the fuzzy behaviourist approach: resolving limit cycle problems in mobile robot navigation.’Intell. Autom. Soft Comput.5 (1) (1999): 31–41.
25.
KrishnaK. M.KalraP. K., ‘Perception and remembrance of the environment during real-time navigation of a mobile robot.’Robot. Autonom. Syst.37 (1) (2001): 25–51.
26.
TuK. Y.BaltesJ., ‘Fuzzy potential energy for a map approach to robot navigation.’Robot. Autonom. Syst.54 (7) (2006): 574–589.
27.
OlunloyoV. O. S.AyomohM. K. O., ‘Autonomous mobile robot navigation using hybrid virtual force field concept.’Eur. J. Sci. Res.31 (2) (2009): 204–228.
28.
KhatibO., ‘Real-time obstacle avoidance for manipulators and mobile robots.’Int. J. Robot. Res.5 (1) (1986): 90–98.
29.
KroghB. H.ThorpeC. E., ‘Integrated path planning and dynamic steering control for autonomous vehicles.’ In Proceedings of IEEE International Conference on Robotics and Automation, San Francisco, California, USA, April 1986, pp. 1664–1669.
30.
JaafarJ.McKenzieE., ‘A fuzzy action selection method for virtual agent navigation in unknown virtual environments.’J. Uncertain Syst.2 (2) (2008): 144–154.
31.
MoravecH. P.ElfesA., ‘High resolution maps from wide angle sonar.’ In Proceedings of IEEE Conference on Robotics and Automation, Washington, DC, USA, March 1985, pp. 116–121.
LuhG.-C.LiuW.-W., ‘Motion planning for mobile robots in dynamic environments using a potential field immune network.’Proc. IMechE, Part I: J. Systems Control and Engineering221 (7) (2007): 1033–1045 DOI: 10.1243/09596518JSCE400.
34.
ParhiD. R.SinghM. K., ‘Real-time navigational control of mobile robots using an artificial neural network.’Proc. IMechE, Part C: J. Mechanical Engineering Science223 (7) (2009): 1713–1725 DOI: 10.1243/09544062JMES1410.
