engleski

Swarm-based Approach to Path Planning Using Honey-bees Mating Algorithm and ART Neural Network

In this paper, an integration of Honey bees mating algorithm (HBMA) and adaptive resonance theory neural network (ART1) for efficient path planning of a mobile robot in a static environment is presented. The robot must find shortest route from given origin to the target position. Moreover, it should be able to memorize the environment and, if it faces known world, execute already learned trajectory found by HBMA solver, or solve the world and memorize the trajectory for the given environment. This is done using Adaptive Resonance Theory based neural network. The network will open a new output class neuron for each unknown environment and connect the solution in form of trajectory to this neuron. If the network recognize environment to some extent similar to already presented environment, appropriate neuron will be immediately triggered and appropriate trajectory executed. This way simulated robot is able to navigate through environment and to continuously increase its knowledge.

Swarm intelligence; artificial neural networks; machine learning; optimization; path planning

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