Experimental Reaction–Diffusion Chemical Processors for Robot Path Planning

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.

[1]  Y. Cohen Electroactive Polymer (EAP) Actuators as Artificial Muscles - Reality , 2001 .

[2]  Osamu Takahashi,et al.  Motion planning in a plane using generalized Voronoi diagrams , 1989, IEEE Trans. Robotics Autom..

[3]  Robert W. McLaren,et al.  Real-time robot path planning using the potential function method , 1993 .

[4]  Alain Liégeois,et al.  Near Optimal Robust Path Planning for Mobile Robots: the Viscous Fluid Method with Friction , 2000, J. Intell. Robotic Syst..

[5]  Petros A. Ioannou,et al.  New Potential Functions for Mobile Robot Path Planning , 2000 .

[6]  Andrew Adamatzky,et al.  On Multitasking in Parallel Chemical Processors: Experimental Findings , 2003, Int. J. Bifurc. Chaos.

[7]  Spyros G. Tzafestas,et al.  Recent algorithms for fuzzy and neurofuzzy path planning and navigation of autonomous mobile robots , 1999, 1999 European Control Conference (ECC).

[8]  Gregory S. Chirikjian,et al.  A new potential field method for robot path planning , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[9]  Jean-Claude Latombe,et al.  Numerical potential field techniques for robot path planning , 1991, Fifth International Conference on Advanced Robotics 'Robots in Unstructured Environments.

[10]  Tien D. Bui,et al.  Robot Path Planning Using Fluid Model , 1998, J. Intell. Robotic Syst..

[11]  Kay Chen Tan,et al.  Evolutionary artificial potential fields and their application in real time robot path planning , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[12]  V. Bonaiuto,et al.  Hardware implementation of a CNN for analog simulation of reaction-diffusion equations , 2001, ISCAS 2001. The 2001 IEEE International Symposium on Circuits and Systems (Cat. No.01CH37196).

[13]  Andrew Adamatzky,et al.  Experimental reaction–diffusion pre-processor for shape recognition , 2002 .

[14]  Narendra Ahuja,et al.  Gross motion planning—a survey , 1992, CSUR.

[15]  Stefan Müller,et al.  Three-dimensional reconstruction of scroll waves in the Belousov-Zhabotinsky reaction using optical tomography , 1996 .

[16]  K. Showalter,et al.  Navigating Complex Labyrinths: Optimal Paths from Chemical Waves , 1995, Science.

[17]  Andrew Adamatzky,et al.  Voronoi-like partition of lattice in cellular automata , 1996 .

[18]  O. Tabata,et al.  Ciliary motion actuator using self-oscillating gel , 2001, Technical Digest. MEMS 2001. 14th IEEE International Conference on Micro Electro Mechanical Systems (Cat. No.01CH37090).

[19]  Tomohiko Yamaguchi,et al.  Finding the optimal path with the aid of chemical wave , 1997 .

[20]  Paolo Arena,et al.  Reaction-diffusion CNN chip. II. Applications , 2000, 2000 IEEE International Symposium on Circuits and Systems. Emerging Technologies for the 21st Century. Proceedings (IEEE Cat No.00CH36353).

[21]  R. Grupen,et al.  Analog VLSI for robot path planning , 1992, [1992] Conference Record of the Twenty-Sixth Asilomar Conference on Signals, Systems & Computers.

[22]  Lionel Tarassenko,et al.  Robot path planning using VLSI resistive grids , 1993 .

[23]  Min Cheol Lee,et al.  Obstacle avoidance for mobile robots using artificial potential field approach with simulated annealing , 2001, ISIE 2001. 2001 IEEE International Symposium on Industrial Electronics Proceedings (Cat. No.01TH8570).

[24]  B. Dickinson,et al.  The complexity of analog computation , 1986 .

[25]  Robin R. Murphy,et al.  Introduction to AI Robotics , 2000 .

[26]  Andrew Adamatzky,et al.  Computing in nonlinear media and automata collectives , 2001 .

[27]  Kianoush Azarm,et al.  Mobile robot path planning and execution based on a diffusion equation strategy , 1992, Adv. Robotics.

[28]  Andrew Adamatzky,et al.  Biologically inspired robots , 2001, SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring.

[29]  Edwin S. H. Hou,et al.  Mobile robot path planning based on hierarchical hexagonal decomposition and artificial potential fields , 1994, J. Field Robotics.

[30]  Andrew Adamatzky,et al.  Phototaxis of mobile excitable lattices , 2002 .

[31]  Andrew Adamatzky,et al.  Chemical Processor for Computation of Skeleton of Planar Shape , 1997 .

[32]  Patrick Marquié,et al.  Experimental nonlinear electrical reaction diffusion lattice , 1998 .

[33]  L. Occhipinti,et al.  Reaction-diffusion CNN design for a new class of biologically-inspired processors in artificial locomotion applications , 1999, Proceedings of the Seventh International Conference on Microelectronics for Neural, Fuzzy and Bio-Inspired Systems.

[34]  Andrew Adamatzky,et al.  Computation of shortest path in cellular automata , 1996 .

[35]  A L Cross,et al.  Three dimensional imaging of the Belousov-Zhabotinsky reaction using magnetic resonance. , 1997, Magnetic resonance imaging.

[36]  A. Adamatzky,et al.  Chemical processor for computation of voronoi diagram , 1996 .