A combination between Adaptive Neuro-fuzzy inference system and neural network for mobile robot dynamic obstacles avoidance

Abstract When a mobile robot has the ability to avoid obstacles while traveling is called an autonomous robot. There are various methods and techniques used to get a collision-free path until gets to the target point. The dynamic obstacle problems are handled by reactive mobile robot navigation techniques. In this paper, the problem of dynamic obstacle avoidance has been addressed by proposing a combination between an Adaptive Neuro-Fuzzy inference system and a Neural network. The proposed system consists of three main parts. The first part was abstracted by using A* algorithm to get the initial path from the start to the goal point. The second part of the system classifies Obstacle(s). The classification estimate whether the obstacle is dangerous and may collide with the mobile robot or not. The relative velocity and distance between the mobile robot and obstacle (s) determine whether the obstacle(s) are dangerous or not. Bayesian regularization Back-Propagation Neural Network is used to train the data for obstacle severity classification. Where obstacle is divided into five zones where zone 1 is dangerous and zone 5 is safe. When obstacle gets into critical regions classified as dangerous. The third part of the system is related to avoiding obstacles if these obstacles are classified as a danger to the mobile robot. The Adaptive Neuro-Fuzzy Inference System has been adopted in the process of avoiding obstacles during the mobile robot motion. Obstacle avoidance is a reaction taken by the robot to avoid collision with dynamic obstacles around it, which are classified as dangerous obstacles by the neural networks. Three important criteria were used as input to the Adaptive Neuro-Fuzzy Inference System, which are the relative speed, distance, and angle between the robot and the obstacle, 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 for avoiding static and dynamic obstacles in a fully known environment. The Adaptive Neuro-Fuzzy Inference System enhances the performance of the proposed controller resulting in the reduction of path length, processing time, and the number of iterations.