2,430 research outputs found
Cellular Automata Applications in Shortest Path Problem
Cellular Automata (CAs) are computational models that can capture the
essential features of systems in which global behavior emerges from the
collective effect of simple components, which interact locally. During the last
decades, CAs have been extensively used for mimicking several natural processes
and systems to find fine solutions in many complex hard to solve computer
science and engineering problems. Among them, the shortest path problem is one
of the most pronounced and highly studied problems that scientists have been
trying to tackle by using a plethora of methodologies and even unconventional
approaches. The proposed solutions are mainly justified by their ability to
provide a correct solution in a better time complexity than the renowned
Dijkstra's algorithm. Although there is a wide variety regarding the
algorithmic complexity of the algorithms suggested, spanning from simplistic
graph traversal algorithms to complex nature inspired and bio-mimicking
algorithms, in this chapter we focus on the successful application of CAs to
shortest path problem as found in various diverse disciplines like computer
science, swarm robotics, computer networks, decision science and biomimicking
of biological organisms' behaviour. In particular, an introduction on the first
CA-based algorithm tackling the shortest path problem is provided in detail.
After the short presentation of shortest path algorithms arriving from the
relaxization of the CAs principles, the application of the CA-based shortest
path definition on the coordinated motion of swarm robotics is also introduced.
Moreover, the CA based application of shortest path finding in computer
networks is presented in brief. Finally, a CA that models exactly the behavior
of a biological organism, namely the Physarum's behavior, finding the
minimum-length path between two points in a labyrinth is given.Comment: To appear in the book: Adamatzky, A (Ed.) Shortest path solvers. From
software to wetware. Springer, 201
Spiking based Cellular Learning Automata (SCLA) algorithm for mobile robot motion formulation
In this paper a new method called SCLA which stands for Spiking based
Cellular Learning Automata is proposed for a mobile robot to get to the target
from any random initial point. The proposed method is a result of the
integration of both cellular automata and spiking neural networks. The
environment consists of multiple squares of the same size and the robot only
observes the neighboring squares of its current square. It should be stated
that the robot only moves either up and down or right and left. The environment
returns feedback to the learning automata to optimize its decision making in
the next steps resulting in cellular automata training. Simultaneously a
spiking neural network is trained to implement long term improvements and
reductions on the paths. The results show that the integration of both cellular
automata and spiking neural network ends up in reinforcing the proper paths and
training time reduction at the same time
Distributed Control of Microscopic Robots in Biomedical Applications
Current developments in molecular electronics, motors and chemical sensors
could enable constructing large numbers of devices able to sense, compute and
act in micron-scale environments. Such microscopic machines, of sizes
comparable to bacteria, could simultaneously monitor entire populations of
cells individually in vivo. This paper reviews plausible capabilities for
microscopic robots and the physical constraints due to operation in fluids at
low Reynolds number, diffusion-limited sensing and thermal noise from Brownian
motion. Simple distributed controls are then presented in the context of
prototypical biomedical tasks, which require control decisions on millisecond
time scales. The resulting behaviors illustrate trade-offs among speed,
accuracy and resource use. A specific example is monitoring for patterns of
chemicals in a flowing fluid released at chemically distinctive sites.
Information collected from a large number of such devices allows estimating
properties of cell-sized chemical sources in a macroscopic volume. The
microscopic devices moving with the fluid flow in small blood vessels can
detect chemicals released by tissues in response to localized injury or
infection. We find the devices can readily discriminate a single cell-sized
chemical source from the background chemical concentration, providing
high-resolution sensing in both time and space. By contrast, such a source
would be difficult to distinguish from background when diluted throughout the
blood volume as obtained with a blood sample
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