The autonomous mobile robot must be capable of avoiding static and dynamic obstacles in the environment and navigating towards the target without any human effort. A valid low-cost path from start to goal is obtained by A* algorithm. Neural network used for Zone classification. The relative values between mobile robot and obstacle are used for classification which are distance, velocity, and angle. Zone1 is very dangerous while zone 5 is not dangerous. If the neural network classifies the obstacle as a dangerous obstacle and activates the controller. The fuzzy logic makes a decision as a reaction of mobile robot to prevent collision. There are three inputs to the fuzzy logic (relative velocity, relative distance, and relative angle) between mobile robot and obstacle. The outputs of fuzzy logic are velocity and steering angle of mobile robot. Static obstacles have been added to the environment in addition to dynamic obstacles to make the environment more complex. Three dangerous dynamic obstacles to the mobile robot are tested. While mobile robot is avoiding one obstacle, another obstacle enters critical zone and becomes dangerous to mobile robot. The mobile robot avoids the second obstacle while it is avoiding the first obstacle. Then the velocities of mobile robot and obstacles have been increased to prove that the proposed system can handle cases with faster velocities. The simulation results for the tested cases shows the capability of the proposed method for avoiding static and dynamic obstacles in fully known environment.

Nowadays, the mobile robot can be seen in different fields of engineering and science. The mobile robot can do some tasks that are so difficult or very risky to be performed by a human. Most of the works currently focus on implementing artificially intelligent algorithms and other algorithms that depend on the behaviour of nature. These approaches have been used in mobile robot navigation along uncertain manner. Mobile robot navigation strategies can be divided into two approaches: the classical approach and reactive approach. The classical approach related to static environment, whiles the reactive navigation is based on an unstructured environment. Path planning is one of the most important parts of the navigation system. In this paper, review and assessment of path planning strategies that can concern with the reactive approach are discussed, because it deal with the problem of dynamic environment. Numerous reactive methods have been introduced. Most of these presented works were concerned with simulation and a few of them have shown experimental implementation. Many papers tried to make a combination between two algorithms or more to increase the efficiency. It is concluded that reactive algorithms need more learning phases, complex in design, and require large memory storage.

Digital image presents information in two-dimensional data, which can be used as feedback measurement for robot visual servoing control. Median filter and morphological operation are used for object detection and extraction its features. Kalman filter is applied for visual measurements that contain noises and uncertainties captured by video camera over the time. Sinusoidal Kalman filter and sinusoidal measurement model is used. The derivations of noise's process and matrices' control are presented. The Kalman filter is tuned by using PSO optimization to produce values closer to the true spatial measurements of the target. A developed PSO is proposed in which adaptive inertia weight chaotic PSO algorithm and velocity constriction factor are used for the porpuse of getting rid from the local and the adjacent optimum convergence. Simulation for tracking object on circular path are presented. Experimental result shows good performance of the proposed method for noisy measurement of the target.

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.