4,259 research outputs found
Maze solvers demystified and some other thoughts
There is a growing interest towards implementation of maze solving in
spatially-extended physical, chemical and living systems. Several reports of
prototypes attracted great publicity, e.g. maze solving with slime mould and
epithelial cells, maze navigating droplets. We show that most prototypes
utilise one of two phenomena: a shortest path in a maze is a path of the least
resistance for fluid and current flow, and a shortest path is a path of the
steepest gradient of chemoattractants. We discuss that substrates with
so-called maze-solving capabilities simply trace flow currents or chemical
diffusion gradients. We illustrate our thoughts with a model of flow and
experiments with slime mould. The chapter ends with a discussion of experiments
on maze solving with plant roots and leeches which show limitations of the
chemical diffusion maze-solving approach.Comment: This is a preliminary version of the chapter to be published in
Adamatzky A. (Ed.) Shortest path solvers. From software to wetware. Springer,
201
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
Memristors
This Edited Volume Memristors - Circuits and Applications of Memristor Devices is a collection of reviewed and relevant research chapters, offering a comprehensive overview of recent developments in the field of Engineering. The book comprises single chapters authored by various researchers and edited by an expert active in the physical sciences, engineering, and technology research areas. All chapters are complete in itself but united under a common research study topic. This publication aims at providing a thorough overview of the latest research efforts by international authors on physical sciences, engineering, and technology,and open new possible research paths for further novel developments
Efficient Implementation of Parallel Path Planning Algorithms on GPUs
In robot systems several computationally intensivetasks can be found, with path planning being one of them.Especially in dynamically changing environments, it is difficult tomeet real-time constraints with a serial processing approach. Forthose systems using standard computers, a promising option is toemploy a GPGPU as a coprocessor in order to offload those taskswhich can be efficiently parallelized. We implemented selectedparallel path planning algorithms on NVIDIA's CUDA platformand were able to accelerate all of these algorithms efficientlycompared to a multi-core implementation. We present the resultsand more detailed information about the implementation of thesealgorithms
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