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Abstract

The proposed navigation system is a combination of two techniques, the Extended Kalman Filter (EKF) and the Grey Woolf Fuzzy Inference System (AGW-NFIS). The EKF is used to improve the accuracy of the position estimation by using the dynamic information obtained from the sensors. The AGW-NFIS, on the other hand, is used as a control mechanism to determine the left and right wheel velocities to be used for obstacle avoidance. The AGW-NFIS is trained using a dataset that includes the obstacle distances and avoidance angles. The robustness of the system is assessed by testing the mobile robot in various conditions. The results of the proposed navigation system have shown to outperform existing strategies, providing a more reliable and efficient solution for mobile robot navigation in obscure and dynamic environments. The combination of the EKF and AGW-NFIS provides a robust solution for obstacle avoidance and navigation. The use of these techniques in mobile robot navigation opens up new possibilities for exploration and operation in difficult environments.