Intelligent motion planning of a mobile robot with dynamic obstacle avoidance

Intelligent navigation in cluttered environment while insuring maximum safety and task efficiency is a challenging subject. Motion planning is an important issue in the field of autonomous mobile robots which makes them capable to travel from one position to another in various environments including both static and dynamic obstacles without any human intervention. This research is conducted for the purpose of designing and programming a mobile robot using two separated fuzzy logic controllers to develop an intelligent algorithm in order to avoid both static and dynamic obstacles. These fuzzy logic controllers play a significant role in mobile robot navigation and obstacle avoidance behavior. In this work, four essential behavior controllers are designed and implemented onto the robot to assist its navigation towards the goal which are: goal reaching behavior, speed control behavior, goal searching behavior and obstacle avoidance behavior. The obstacle avoidance behavior is divided into two individual behaviors which are static obstacle avoidance behavior and dynamic obstacle avoidance behavior where these behaviors are controlled by an artificial intelligence (AI) algorithm. In order to design obstacle avoidance behavior, Sugeno fuzzy logic was applied. The simulation of this research was done by MATLAB software where a mobile robot and some experimental environments with different complexity were created. Several navigation tests were conducted and the robot's behavior were observed as well. Analysis of the robot's performance validated the effectiveness of the proposed controllers and the robot could successfully navigate to reach the goal through all experimental environments.