Experimental Reaction–Diffusion Chemical Processors for Robot Path Planning

Journal of Intelligent and Robotic Systems - 2003
Andrew Adamatzky1, Benjamin de Lacy Costello2, Chris Melhuish1, Norman Ratcliffe2
1Faculty of Computing, Engineering and Mathematical Sciences, University of the West of England, Bristol, UK
2Faculty of Applied Sciences, University of the West of England, Bristol, UK

Tóm tắt

In this paper we discuss the experimental implementation of a chemical reaction–diffusion processor for robot motion planning in terms of finding the shortest collision-free path for a robot moving in an arena with obstacles. These reaction–diffusion chemical processors for robot navigation are not designed to compete with existing silicon-based controllers. These controllers are intended for the incorporation into future generations of soft-bodied robots built of electro- and chemo-active polymers. In this paper we consider the notion of processing as being implicit in the physical medium constituting the body of a ‘soft’ robot. This work therefore represents some early steps in the employment of excitable media controllers. An image of the arena in which the robot is to navigate is mapped onto a thin-layer chemical medium using a method that allows obstacles to be represented as local changes in the reactant concentrations. Disturbances created by the ‘objects’ generate diffusive and phase wave fronts. The spreading waves approximate to a repulsive field generated by the obstacles. This repulsive field is then inputted into a discrete model of an excitable reaction–diffusion medium, which computes a tree of shortest paths leading to a selected destination point. Two types of chemical processors are discussed: a disposable palladium processor, which executes arena mapping from a configuration of obstacles, given before an experiment and, a reusable Belousov–Zhabotinsky processor which allows for online path planning and adaptation for dynamically changing configurations of obstacles.

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Tài liệu tham khảo

Adamatzky, A.: 1994, Reaction–diffusion algorithm for constructing discrete generalized Voronoi diagram, Neural Networks World 3, 241–254.

Adamatzky, A.: 1996a, Voronoi-like lattice partition of lattice in cellular automata, Math. Comput. Modelling 23, 51–66.

Adamatzky, A.: 1996b, Computation of shortest path in cellular automata, Math. Comput. Modelling 23, 105–113.

Adamatzky, A.: 2001, Computing in Nonlinear Media and Automata Collectives, Institute of Physics Publishing.

Adamatzky, A. and Melhuish, C.: 2001a, Phototaxis of mobile excitable lattices, Chaos Solitons Fractals 13, 171–184.

Adamatzky, A. and Melhuish, C.: 2001b, Towards the design of excitable lattice controllers for (nano)robots, Smart Engrg. Systems Design 3, 265–277.

Adamatzky, A. and Tolmachiev, D.: 1997, Chemical processor for computation of skeleton of planara shape, Adv. Mater. Optics Electr. 7, 135–139.

Adamatzky, A., De Lacy Costello, B., and Ratcliffe, N. M.: 2002a, Experimental reaction–diffusion pre-processor for shape recognition, Phys. Lett. A 297, 344–352.

Adamatzky, A., De Lacy Costello, B., Melhuish, C., Rambidi, N., Ratcliffe, N., and Wessnitzer, J.: 2002b, Excitable chemical controllers for robots, in: Proc. EPSRC/BBSRC Int. Workshop Biologically-Inspired Robotics: The Legacy of W. Grey Walter, 14–16 August 2002, Bristol, UK.

Agladze, K., Magome, N., Aliev, R., Yamaguchi, T., and Yoshikawa, K.: 1997, Finding the optimal path with the aid of chemical wave, Phys. D 106, 247–254.

Arena, P., Branciforte, M., Di Bernardo, G., Lavorgna, M., and Occhipinti, L.: 2000, Reaction–diffusion CNN chip, in: Proc. of the 2000 IEEE Internat. Sympos. on Circuits and Systems, IEEE, Vol. 3, pp. 419–422, 427–430.

Bar-Cohen, Y. (ed.): 2001, Electroactive Polymer (EAP) Actuators as Artificial Muscles, SPIE Press.

Barraquand, J., Langlois, B., and Latombe, J. C.: 1992, Numerical potential field techniques for robot path planning, IEEE Trans. Systems Man Cybernet. 22, 224–241.

Binczak, S., Comte, J. C., Michaux, B., Marquie, P., and Bilbault, J. M.: 1998, Experimental nonlinear electrical reaction diffusion lattice, Electron. Lett. 34, 1061–1062.

Bonaiuto, V., Maffucci, A., Miano, G., Salerno, M., Sargeni, F., Serra, P., and Visone, C.: 2001, Hardware implementation of a CNN for analog simulation of reaction–diffusion equations, in: ISCAS 2001, Proc. of the 2001 IEEE Internat. Sympos. on Circuits and Systems, IEEE, Vol. 2, pp. 485–488.

Branciforte, M., Di Bernardo, G., Doddo, F., and Occhipinti, L.: 1999, Reaction–diffusion CNN design for a new class of biologically-inspired processors in artificial locomotion applications, in: Proc. of 17th Internat. Conf. on Microelectronics for Neural, Fuzzy and Bio-Inspired Systems, IEEE, pp. 69–76.

Cross, A. L., Armstrong, R. L., Gobrecht, C., Paton, M., and Ware, C.: 1997, Three-dimensional imaging of the Belousov–Zhabotinsky reaction using magnetic resonance, Magnetic Resonance Imaging 15, 719–725.

De Lacy Costello, B. P. J. and Adamatzky, A. I.: 2002, unpublished data.

De Lacy Costello, B. P. J. and Adamatzky, A. I.: 2003, On multi-tasking in parallel chemical processors: Experimental findings, Internat. J. Bifurcation Chaos 13, in press.

Fourie, C. J.: 2000, Intelligent path planning for a mobile robot using a potential field algorithm, in: Proc. of the 29th Internat. Sympos. on Robotics, Advanced Robotics, Beyond 2000, DMG Business Media, Redhill, UK, 1998, pp. 221–224.

Ge, S. S. and Cui, Y. J.: 2000, New potential functions for mobile robot path planning, IEEE Trans. Robotics Automat. 16, 615–620.

Hou, E. S. H. and Zheng, D.: 1994, Mobile robot path planning based on hierarchical hexagonal decomposition and artificial potential fields, J. Robotic Systems 11, 605–614.

Hussien, B. and McLaren, R. W.: 1993, Real-time robot path planning using the potential function method, Automation Construct. 2, 241–250.

Hwang, Y. K. and Ahuja, N.: 1992, Gross motion planning – A survey, ACM Computing Surveys 2, 219–291.

Kennedy, B., Melhuish, C., and Adamatzky, A.: 2001, Biologically inspired robots, in: Y. Bar-Cohen (ed.), Electroactive Polymer (EAP) Actuators as Artificial Muscles, SPIE Press.

Li, Z. X. and Bui, T. D.: 1998, Robot path planning using fluid model, J. Intelligent Robotic Systems 21, 29–50.

Louste, C. and Liegeois, A.: 2000, Near optimal robust path planning for mobile robots: the viscous fluid method with friction, J. Intelligent Robotic Systems 27, 99–112.

Marshall, G. F. and Tarassenko, L.: 1994, Robot path planning using VLSI resistive grids, IEE Proc. Vision Image Signal Process. 141, 267–272.

Murphy, R.: 2000, An Introduction to AI Robotics, MIT Press.

Park, M. G., Jeon, K. H., and Lee, M. C.: 2001, Obstacle avoidance for mobile robots using artificial potential field approach with simulated annealing, in: Proc. of 2001 IEEE Internat. Sympos. on Industrial Electronics, IEEE, Piscataway, NJ, USA, Vol. 3, pp. 1530–1535.

Schmidt, G. K. and Azarm, K.: 1993, Mobile robot path planning and execution based on a diffusion equation strategy, Adv. in Robotics 7, 479–490.

Serradilla, F. and Maravall, D.: 1996, A navigation system for mobile robots using visual feedback and artificial potential fields, in: Cybernetics and Systems' 96, Proc. of 13th European Meeting on Cybernetics and Systems Research, Austrian Soc. Cybernetic Studies, Vienna, Vol. 2, pp. 1159–1164.

Stan, M. R., Burleson, W. P., Connolly, C. I., and Grupen, R. A.: 1994, Analog VLSI for robot path planning, J. VLSI Signal Processing 8, 61–73.

Steinbock, O., Tóth, A. and Showalter, K.: 1995, Navigating complex labyrinths: Optimal paths from chemical waves, Science 267, 868–871.

Stock, D. and Müller, S. C.: 1996, Three-dimensional reconstruction of scroll waves in the Belousov–Zhabotinsky reaction using optical tomography, Phys. D 96, 396–403.

Tabata, O., Hirasawa, H., Aoki, S., Yoshida, R., and Kokufuta, E.: 2002, Ciliary motion actuator using self-oscillating gel, Sensors Actuators A 95, 234–238.

Takahashi, O. and Schilling, R. J.: 1989, Motion planning in a plane using generalized Voronoi diagram, IEEE Trans. Robotics Automat. 5, 143–150.

Tolmachev, D. and Adamatzky, A.: 1996, Chemical processor for computation of Voronoi diagram, Adv. in Mater. Opt. Electron. 6, 191–196.

Tzafestas, C. S. and Tzafestas, S. G.: 1999, Recent algorithms for fuzzy and neurofuzzy path planning and navigation of autonomous mobile robots, Systems Sci. 25, 25–39.

Vadakkepat, P., Tan, K. C., and Liang, W. M.: 2000, Evolutionary artificial potential fields and their application in real time robot path planning, in: Proc. of 2000 Congress. on Evolutionary Computation. CEC00, IEEE, Piscataway, NJ, Vol. 1, pp. 256–263.

Vergis, A., Steiglitz, K., and Dickinson, B.: 1986, The complexity of analog computation, Math. Comput. Simulation 28, 91–113.

Wang, Y. F. and Chirikjian, G. S.: 2000, A new potential field method for robot path planning, in: Proc. IEEE Internat. Robotics and Automation Conference, San Francisco, CA, IEEE, Piscataway, NJ, pp. 977–982.

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Journal of Intelligent and Robotic Systems

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