This paper proposes a potential field immune network (PFIN) for dynamic motion planning of mobile robots in an unknown environment with moving obstacles and a fixed/moving goal. The velocity obstacle method is utilized to determine the imminent collision obstacle. Subsequently, PFIN is implemented to guide robots to avoid collision with the most dangerous objects at every time instant. The overall response of the immune network is calculated with the aid of fuzzy logic and genetic algorithms. Simulation results are presented to verify the effectiveness of the proposed architecture in dynamic environments with single and multiple fixed/moving obstacles.
SanbornJ.HendlerJ.A model of reaction for planning in dynamic environmentsInt. J. AI in Engng, 1988, 3 (2), 95–101.
2.
FujimuraK.SametH.A hierarchical strategy for path planning among moving obstaclesIEEE Trans. Robotics and Automn, 1989, 5 (1), 61–69.
3.
FraichardT.Dynamic trajectory planning with dynamic constraints: A state-time space approachIn Proceedings of the IEEE/RSJ International Conference on Intelligent robots and systems, Yokohama, Japan, 1993, pp. 1393–400.
4.
FerrariC.PagelloE.OtaJ.AraiT.Multirobot motion coordination in space and timeRobotics and Autonomous Syst., 1998, 25 (2), 219–229.
5.
FujimoriA.OgawalY.NikiforukP. N.A modification of cooperative collision avoidance for multiple mobile robots using the avoidance circleProc. Instn Mech. Engrs, Part I: J. Systems and Control Engineering, 2002, 216 (3), 291–299.
6.
FujimoriA.Navigation of mobile robots with collision avoidance for moving obstaclesProc. IMechE, Part I: J. Systems and Control Engineering, 2005, 219 (I1), 99–110.
7.
SgorbissaA.ArkinR. C.Local navigation strategies for a team of robotsRobotica, 2003, 21 (5), 461–473.
8.
MucientesM.IglesiasR.RegueiroC. V.BugarínA.CariñenaP.BarroS.Fuzzy temporal rules for mobile robot guidance in dynamic environmentsIEEE Trans. Syst., Man, and Cybernetics - Part C: Applic. and Rev., 2001, 31 (3), 391–398.
9.
KrishnaK. M.KalraP. K.Detection, tracking and avoidance of multiple dynamic objectsJ. Intell. and Robotics Syst., 2002, 33, 371–408.
10.
FioriniP.ShillerZ.Motion planning in dynamic environments using the relative velocity paradigmIn Proceedings of the IEEE International Conference on Robotics and automation, Atlanta, Georgia, USA, 1993, pp. 560–565.
11.
FioriniP.ShillerZ.Motion planning in dynamic environments using velocity obstaclesInt. J. Robotics Res., 1998, 17, 760–772.
12.
PrasslerE. L.ScholzJ.FioriniP.Navigating a robotic wheelchair in a railway station during rush hourJ. Robotics Res., 1999, 18 (7), 711–726.
13.
PrasslerE.ScholzJ.FiorniP.A robotic wheelchair for crowded public environmentsIEEE Robotics and Automn Mag., 2001, 7 (1), 38–45.
14.
ShillerZ.LargeF.SekhavatS.Motion planning in dynamic environments: Obstacles moving along arbitrary trajectoriesIn Proceedings of the IEEE International Conference on Robotics and automation, Seoul, Korea, 2001, pp. 3716–3722.
15.
YamamotoM.ShimadaM.MohriA.On-line navigation of mobile robot under the existence of dynamically moving multiple obstaclesIn Proceedings of the International Symposium on Assembly and task planning soft research park, Fukuoka, Japan, 2001, pp. 28–29.
16.
KlugeB.Recursive probabilistic velocity obstacles for reflective navigation. In Proceedings of the 1st International Workshop on Advances in Service Robots, Bardolino, Italy, 2003.
17.
ChakravarthyA.GhoseD.Obstacle avoidance in a dynamic environment: A collision cone approachIEEE Trans. on Syst., Man, and Cybernetics - Part A: Syst. and Humans, 1998, 28 (5), 562–574.
18.
YungN. H.C.YeC.Avoidance of moving obstacles through behavior fusion and motion predictionIn Proceedings of the IEEE International Conference on Systems, man, and cybernetics, San Diego, California, USA, 1998, pp. 3424–3429.
19.
UshimiN.YamamotoM.InoueJ.SugimotoT.AraokaM.MatsuokaT.KirikiT.YamaguchiY.HasegawaT.MohriA.On-line navigation of mobile robot among moving obstacles using ultrasonic sensors In RoboCup 2001 (Eds BirkA.CoradeschiS.TadokoroS.), 2002, LNAI 2377, pp. 477–483.
20.
QuZ.WangJ.PlaistedC. E.A new analytical solution to mobile robot trajectory generation in the presence of moving obstaclesIEEE Trans. Robotics and Automn, 2004, 20 (6), 978–993.
21.
ConnR. A.KamM.Robot motion planning on N-dimensional star worlds among moving obstaclesIEEE Trans. Robotics and Automn, 1998, 14 (2), 320–325.
22.
VadakkepatP.TanK. C.WangM. L.Evolutionary artificial potential fields and their application in real time robot path planningIn Proceedings of the Congress on Evolutionary computation, San Diego, California, USA, 2000, pp. 256–263.
23.
GeS. S.CuiY. J.Dynamic motion planning for mobile robots using potential field methodAutonomous Robots, 1989, 13 (3), 207–222.
24.
PotyA.MelchiorP.OustaloupA.Dynamic path planning for mobile robots using fractional potential fieldIn Proceedings of the IEEE International Symposium on Control, communications and signal processing, Hammamet, Tunisia, 2004, pp. 557–561.
25.
MunasingheS. R.OhC.LeeJ. J.KhatibO.Obstacle avoidance using velocity dipole field method. In International Conference on Control, automation, and systems, Kintex, Korea, 2005, pp. 1657–1661.
26.
RateringS.GiniM.Robot navigation in a known environment with unknown moving obstaclesAutonomous Robots, 1995, 1 (2), 149–165.
27.
ZavlangasP. G.TzafestasS. G.AlthoeferK.Fuzzy obstacle avoidance and navigation for omnidirectional mobile robots. In European Symposium on Intelligent techniques, Aachen, Germany, 2000, pp. 375–382.
28.
KhatibO.Real-time obstacle avoidance for manipulators and mobile robotsInt. J. Robotics Res., 1986, 5 (1), 90–98.
29.
ParkM. G.LeeM. C.Artificial potential field based path planning for mobile robots using a virtual obstacle conceptIn Proceedings of the IEEE/ASME International Conference on Advanced intelligent mechatronics, Kobe, Japan, 2003, pp. 735–740.
30.
CosíoF. A.CastañedaM. A. P.Autonomous robot navigation using adaptive potential fieldsMathl and Computer Modeling, 2004, 40 (9-10), 1141–1156.
31.
LuhG.-C.LiuW.-W.Reactive immune network based mobile robot navigationLect. Notes in Computer Sci., 2004, 3239, 119–132.
32.
AbeY.MatsuoY.Iterative algorithm for the decomposed cooperative collision avoidance method. In SICE Annual Conference, Sapporo, Japan, 2004, pp. 2095–2098.
33.
DasguptaD.Artificial immune systems and their applications, 1999 (Springer-Verlag, Berlin-Heidelberg).
34.
de CastroL. N.JonathanT.Artificial immune systems: a new computational intelligence approach, 1999 (Springer-Verlag, London Berlin Heidelberg).
35.
CorneD.DorigoM.GloverF.New ideas in optimization. Part 3. Immune system methods (Eds CorenD.DorigoM.GloverF.), 1999 (McGraw-Hill, New York).
36.
IshiguroA.KondoT.WatanabeY.UchikawaY.Dynamic behavior arbitration of autonomous mobile robots using immune networkIn Proceedings of the IEEE International Conference on Evolutionary computation, Perth, Australia, 1995, pp. 722–727.
37.
IshiguroA.WatanabeY.UchikawaY.An immunological approach to dynamic behavior control for autonomous mobile robotsIn Proceedings of the IEEE International Conference on Intelligent robots and systems, Pittsburgh, Pennyslvania, USA 1995, pp. 495–500.
38.
LeeD.-J.LeeM.-J.ChoiY.-K.KimS.Design of autonomous mobile robot action selector based on a learning artificial immune network structureIn Proceedings of the 5th Symposium on Artificial life and robotics, Oita, Japan, 2000, pp. 116–119.
39.
VargasP. A.de CastroL. N.MichelanR.Von ZubenF. J.Implementation of an immunogenetic network on a real Khepera II robotIn Proceedings of the IEEE Congress on Evolutionary computation, Canberra, Australia, 2003, pp. 420–426.
40.
RoittI.BrostoffJ.MaleD. K.Immunology, 5th edition, 1998 (Mosby International Limited, London).
41.
ForrestS.PerelsonA. S.AllenL.CherukuriR.Self-nonself discrimination in a computerIn Proceedings of IEEE Symposium on Research in security and privacy, Oakland, California, USA, 1994 May, vols 16-18, pp. 202–212.
42.
DasguptaD.Artificial neural networks and artificial immune systems: Similarities and differencesIn Proceedings of the IEEE International Conference on System, man, and cybernetics, Orlando, Florida, USA, 1997, pp. 873–878.
FarmerJ. D.PackardN. H.PerelsonA. S.The immune system adaptation, and machine learningPhysica, 1986, 22-D, 184–204.
45.
HoffmannG. W.The immune system: A neglected challenge for network theorists. In IEEE International Symposium on Circuits and systems, Portland, Oregon, USA, 1989, pp. 1620–1623.
46.
CarneiroJ.CoutinhoA.FaroJ.StewarJ.A model of the immune network with B-T cell cooperation: I - prototypical structures and dynamicsJ. Theor. Biol., 1996, 182 (44), 513–529.
47.
HightowerR.ForrestS.PerelsonA. S.The evolution of emergent organization in immune system gene librariesIn Proceedings of 6th International Conference on Genetic algorithms, San Francisco, California, USA, 1995, pp. 344–350.
48.
TimmisJ.NealM.HuntJ.Data analysis using artificial immune systems, cluster analysis and Kohonen networks: Some comparisons. In IEEE International Conference on Systems, man, and cybernetics, Tokyo, Japan, 1999, pp. 922–927.
49.
O'RourkeJ.BadlerN.Decomposition of three dimensional objects into spheresIEEE Trans. Pattern Analysis Mach. Intell., 1979, 1 (3), 295–305.
50.
ChazelleB.Approximation and decomposition of shapes In Advances in robotics. Vol. I. Algorithmic and geometric aspects of robotics (Eds SchwartzJ. T.YapC. K.), 1987, pp. 145–185 (Lawrence Erlbaum, Hillsdale, New Jersey).
