Robust mobile robot navigation in cluttered environments based on hybrid adaptive neuro-fuzzy inference and sensor fusion

Collision-free navigation of mobile robots is a challenging task, especially in unknown environments, and various studies have been carried out in this regard. However, the previous studies have shortcomings, such as low performance in cluttered and unknown environments, high computational costs, and multiple controller models for navigation. This paper proposes an adaptive neuro-fuzzy inference system (ANFIS) and global positioning system (GPS) for control and navigation to overcome these problems. The proposed method automates the navigation of a mobile robot while averting obstacles in unknown and densely cluttered environments. Furthermore, the mobile robots' global path planning and steering are controlled using GPS and heading sensor data fusion to achieve the target coordinates. A fuzzy inference system (FIS) is adopted to model obstacle avoidance where distance sensors data is converted into fuzzy linguistics. Moreover, a type-1 Takagi-Sugeno FIS is used to train a five-layered neural network for the local planning of the robot, and ANFIS parameters are tuned using a hybrid learning method. In addition, an algorithm is designed to generate a dataset for testing and training the ANFIS controller. All the testing and training are conducted in MATLAB, while simulations are carried out using CoppeliaSim. Comprehensive experiments are performed to validate the robustness of the proposed method. The results of the experiments show that the proposed approach outperforms various state-of-the-art neuro-fuzzy, CS-ANFIS, multi-ANFIS, and hybrid ANFIS navigation and obstacle avoidance methods in finding a near-optimal path in unknown environments.