Multi-layer perceptron neural network mobile robot navigator in unknown environment - Mohammed Rabeea Hashim Al-Dahhan; Ruqayah Rabeea Al-Dahhan; Ali Tariq Radeef (Institute of Advanced Engineering and Science, 2023)
Recently, navigation in an unknown environment without hitting obstacles was considered a big challenge faced by researchers. The difficulty in finding a good mathematical model for the different systems is deciding to use artificial intelligent controllers to control the mobile robot movement. In this paper, designing two multi-layer-perceptron neural networks (MLP-NN) was done to control the movement of mobile robots in an unknown environment. The first MLP-NN is to control the linear velocity on the x-axis and angular velocity of the robot’s movement while the other MLP-NN is designed to avoid the static and dynamic obstacles faced by the robot while navigating in an unknown environment. The results show each controller's advantages in performing navigation tasks and avoiding obstacles in different environments.