Obstacle Avoidance Using Modified Hopfield Neural Network for Multiple Robots
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In this paper, dynamic path planning of two mobile robots using a modified Hopfield neural network is studied. An area which excludes obstacles and allows gradually changing of activation level of neurons is derived in each step. Next moving step can be determined by searching the next highest activated neuron. By learning repeatedly, the steps will be generated from starting to goal points. A path will be constructed from these steps. Simulation showed the constructed paths of two mobile robots, which are moving across each other to their goals.