a combination of adaptive neuro-fuzzy inference system and neural network for mobile robot dynamic obstacle avoidance

A mobile robot must be autonomous to avoid obstacles while traveling towards the target. dynamic obstacle avoidance remains a significant challenge in mobile robotics. although reactive navigation strategies have been applied to address this problem, relying on the single-stage module often results in limited efficiency and restricted overall performance. this paper proposes combining an adaptive neuro-fuzzy inference system (anfis) and a neural network (nn). the data for obstacle severity classification were used to train the neural network. the relative velocity and distance between the mobile robot and obstacles determine the zone. zone 1 is dangerous, and zone 5 is safe. this paper uses the anfis to avoid obstacles during the mobile robot's motion and to avoid collisions. based on our empirical study, three essential features have been considered in this paper: the relative speed, distance, and angle between the robot and the obstacle as inputs to the obstacle avoidance system anfis. the output was a suggested steering angle and speed for the mobile robot. the simulation results for the tested cases show the capability of the proposed controller to avoid static and dynamic obstacles in a fully known environment. our results show that the anfis system enhances the proposed controller's performance, reducing path length, processing time, and the number of iterations compared to state-of-the-art research papers. the proposed work demonstrated better performance in path length reduction (approximately 6%) and time taken reduction to reach the target, which is reduced by about 60%.