Autonomous Mobile Robot Localization And Navigation In Unknown Environment

Recently, researchers have begun to investigate intelligent environments for robot application. In this work, a robot and its work environment can be considered to an integrated system. Currently, such robot-environment systems are designed through lots of research, with the final performance of the system greatly dependent on the experience and preferences of the designer. This dissertation investigates a way to improve on this situation by looking at the complexity of an environment from perspective of a robot. The objective of this research is to develop a method to evaluate environmental complexity and then use this information to help design a robot-environment system, and help robot's navigation.However in case of noisy environment, the range measurements have greater inaccuracy. In such cases, localization using any algorithm for the localization can provide some inaccuracy. To rectify such erroneous localization situations, Extended Kalman Filters are used to estimate the position. The Extended Kalman Filter has been used the process for estimation of coordinates is a non-linear process. The EKF is a recursive filter which only needs the information from the previous state to predict the next state. The dissertation implements the Extended Kalman Filter Localization algorithm for the preparation of the research.Here develops a Fuzzy logic (FL) algorithm to solve the robot motion path problem by incorporating fuzzy logic control and priority-based behavior control. First, four basic behaviors for mobile robot navigation are proposed, such as goal seeking, obstacle avoidance, tracking, and deadlock disarming, which are implemented through a fuzzy logic controller. In particular for'U'or'V'shaped obstacles, where mobile robot may be trapped, a Path Remembering Behavior is employed to protect robots from re-entering such areas via the Creation of Virtual Wall. The Matlab simulation results show that the proposed algorithm is effective in navigating mobile robots in a complex and unknown circumstance, and it has good robustness and adaptability to the uncertainties involved in sensor.Recently, there has been extensive work on the improvement of the fuzzy controllers for mobile robots. In this dissertation, it provides some means to realize evolutionary optimization as a promising method for developing fuzzy controllers. However, there remains still much investigation on the evolutionary fuzzy controller because most of the previous works have not seriously attempted to analyze this question. This paper develops a fuzzy logic controller for a mobile robot with a Genetic Algorithm (GA) in simulation environments and analyzes the behaviors of the controller. Experimental results show that appropriate control mechanisms of the fuzzy controller are obtained by evolution. The controller has evolved well enough to smoothly drive the robot in different environments.