×

Cooperative behavior of nano-robots as an analogous of the quantum harmonic oscillator. (English) Zbl 1186.68473

Summary: A multi-robot system that consists of N nano-robots is studied. It is assumed that the robots correspond to diffusing particles, and interact to each other as the theory of Brownian motion predicts. Brownian motion is the analogous of the quantum harmonic oscillator (Q.H.O.), i.e., of Schrödinger’s equation under harmonic (parabolic) potential. It is shown that the motion of the robots can be described by Langevin’s equation which is a stochastic linear differential equation. It is proved that Langevin’s equation is a generalization of conventional gradient algorithms. Therefore the kinematic models of mobile robots which follow conventional gradient algorithms can be considered as a subcase of the kinematic models which are derived from the diffusion analogous of the Q.H.O model.

MSC:

68T40 Artificial intelligence for robotics
60G07 General theory of stochastic processes
70B05 Kinematics of a particle
70F45 The dynamics of infinite particle systems
81Q05 Closed and approximate solutions to the Schrödinger, Dirac, Klein-Gordon and other equations of quantum mechanics
82C22 Interacting particle systems in time-dependent statistical mechanics
82C31 Stochastic methods (Fokker-Planck, Langevin, etc.) applied to problems in time-dependent statistical mechanics
93E03 Stochastic systems in control theory (general)
93A14 Decentralized systems
93A30 Mathematical modelling of systems (MSC2010)
Full Text: DOI

References:

[1] Hog, T.: Coordinating microscopic robots in viscous fluids. Auton. Agents Multi-agent Syst. 14, 271–305 (2007) · doi:10.1007/s10458-006-9004-3
[2] Louste, C., Liegeois, A.: Near optimal robust path planning for mobile robots: the viscous fluid method with friction. J. Intell. Robot. Syst. 27, 99–112 (2000) · Zbl 0962.93068 · doi:10.1023/A:1008102230551
[3] Adamatzsky, A., de Lacy Costello, B., Melhuish, C., Ratcliffe, N.: Experimental reaction-diffusion chemical processors for robot path planning. J. Intell. Robot. Syst. 37, 233–249 (2003) · Zbl 1057.68117 · doi:10.1023/A:1025414424756
[4] Tan, X.: Self-organization of autonomous systems via Langevin equation. In: 46th IEEE Intl. Conference on Decision and Control, pp. 1435–1440, New Orleans, 12–14 December 2007
[5] Adamatzsky, A., Arena, P., Basile, A., Carmona-Galaán, R., de Lacy Costello, B., Fortuna, L., Frasca, M., Rodriígez-Vázquez, A.: Reaction–Diffusion navigation robot control: from chemical to VLSI analogic processors. IEEE Trans. Circuits Syst.-I: Regular Papers 51(5), 926–938 (2004) · doi:10.1109/TCSI.2004.827654
[6] Schmidt, G., Neubauer, W.: High speed robot path planning in time-varying environment employing a diffusion equation strategy. In: Tzafestas, S.G. (ed.) Robotic Systems, pp. 207–215. Kluwer, Dordrecht (1992)
[7] Faris, W.G.: Diffusion, Quantum Theory, and Radically Elementary Mathematics. Princeton University Press, Princeton (2006) · Zbl 1104.81003
[8] Ozhigov, Y.: Amplitude quanta in multi particle system simulation. Russ. Microelectron. 35(1), 53–65 (2006) · doi:10.1134/S1063739706010070
[9] Ozhigov, Y.: Simulation of quantum dynamics via classical collective behavior. Russ. Microelectron. 36(3), 193–202 (2007) · doi:10.1134/S1063739707030080
[10] Levine, H., Rappel, W.J.: Self-organization in systems of self-propelled particles. Phys. Rev. E 63 (2000) · Zbl 1027.92006
[11] Gitterman, M.: The Noisy Oscillator: The First Hundred Years, from Einstein until Now. World Scientific, Singapore (2005) · Zbl 1145.34002
[12] Duflo, M.: Algorithmes stochastiques. In: Mathématiques et Applications, vol. 23. Springer, New York (1996) · Zbl 0882.60001
[13] Benvensite, A., Metivier, P., Priouret, P.: Adaptive algorithms and stochastic approximations. In: Applications of Mathematics Series, vol. 22. Springer, New York (1990)
[14] Gazi, V., Passino, K.: Stability analysis of social foraging swarms. IEEE Trans. Syst. Man Cybern. Part B 34, 539–557 (2004) · doi:10.1109/TSMCB.2003.817077
[15] Rigatos, G.G.: Coordinated motion of autonomous vehicles with the use of a distributed gradient algorithm. In: Applied Mathematics and Computation. Elsevier, Amsterdam (2008) · Zbl 1228.93084
[16] Rigatos, G.G.: Cooperative behavior of mobile robots as a macro-scale analogous of the quantum harmonic oscillator. In: IEEE SMC 2008 Intl. Conference, Singapore, September 2008
[17] Rigatos, G.G.: Distributed gradient and particle swarm optimization for multi-robot motion planning. In: Robotica. Cambridge University Press, Cambridge (2008)
[18] Klebaner, F.C.: Introduction to Stochastic Calculus with Applications. Imperial College Press, London (2005) · Zbl 1077.60001
[19] Comet, F., Meyre, T.: Calcul Stochastique et Modèles de Diffusion. Dunod, Paris (2006)
[20] Rigatos, G.G., Tzafestas, S.G.: Quantum learning for neural associative memories. Fuzzy Sets Syst. 13(157), 1797–1813 (2006) · Zbl 1100.68097 · doi:10.1016/j.fss.2006.02.012
[21] Basseville, M., Nikiforov, I.: Detection of Abrupt Changes: Theory and Applications. Prentice-Hall, Englewood Cliffs (1993)
[22] Cohen-Tannoudji, C., Diu, B., Laloë, F.: Mécanique Quantique I. Hermann, Paris (1998)
[23] Müller, G.: Quantum Mechanics: Symmetries, 2nd edn. Springer, New York (1998)
[24] Rigatos, G.G., Tzafestas, S.G.: Parallelization of a fuzzy control algorithm using quantum computation. IEEE Trans. Fuzzy Syst. 10, 451–460 (2002) · doi:10.1109/TFUZZ.2002.800690
[25] Dubois, D., Foulloy, L., Mauris, G., Prade, H.: Probability-possibility transformations, triangular fuzzy sets and probabilistic inequalities. Reliab. Comput. 10(4), 273–297 (2004) · Zbl 1043.60003 · doi:10.1023/B:REOM.0000032115.22510.b5
[26] Rigatos, G.G., Tzafestas, S.G.: Neural structures using the eigenstates of the quantum harmonic oscillator. Open Syst. Inf. Dyn. 13, 27–41 (2006) · Zbl 1151.68594 · doi:10.1007/s11080-006-7265-6
[27] Rigatos, G.G.: Quantum wave-packets in fuzzy automata and neural associative memories. Int. J. Mod. Phys. C 18(10), 1551–1569 (2007) · Zbl 1200.82054 · doi:10.1142/S012918310701156X
[28] Tzafestas, S.G., Rigatos, G.G.: Stability analysis of an adaptive fuzzy control system using Petri Nets and learning automata. Math. Comput. Simul. 51(3), 315–341 (2000) · doi:10.1016/S0378-4754(99)00127-5
[29] Rigatos, G.G.: Open-loop control of particle systems based on a model of coupled stochastic oscillators. In: ICQNM 2009, 3rd Intl. Conference on Quantum, Nano and Micro Technologies, Cancun, February 2009
[30] Rouchon, P.: Flatness-based control of oscillators. Z. Angew. Math. Mech. 85(6), 411–421 (2005) · Zbl 1079.93026 · doi:10.1002/zamm.200410194
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.