The complete coverage for the vacuum cleaner robot using pulse-coupled neural network in dynamic environments

Abstract

The vacuum cleaner robot requires the artificial intelligence to solve the problem of sweeping of the entire environment areas taking into account some factors such as the time and the length of the generated path. This task is known as the complete region coverage navigation (CRCN). In this paper, to resolve the problem of CRCN in a room environment, we propose the pulse-coupled neural network (PCNN) model. The latter is based on the firing event in which pulses are emitted from a neuron to another until it fires all the neurons. Each neuron has only local lateral connections with its neighbors. In addition, this mechanism helps the robot to pass through every part of the dynamic environment by avoiding obstacles using different sensors. The results of simulation and comparison studies demonstrate the effectiveness and efficiency of the proposed approach.

Keywords

Artificial intelligence complete region coverage navigation (CRCN)pulse-coupled neural network (PCNN)vacuum cleaner robot

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