Vulla Mohammed Habeeb; International Journal of Advance Research, Ideas and Innovations in Technology © 2018, www.IJARIIT.com All Rights Reserved Page | 16 ISSN: 2454-132X Impact factor: 4.295 (Volume 4, Issue 3) Available online at: www.ijariit.com Autonomous robot navigation in obstacles based environment Mohammed Habeeb Vulla habeebvulla@gmail.com Dravidian University, Kuppam, Andhra Pradesh ABSTRACT This Article presents an on-line path planning algorithm for autonomous robot’s navigation system. Path planning is one of the most important topics in artificial intelligence and robotics navigation field. It can be used in many applications such as autonomous mobile robot navigation, network routing, video game artificial intelligence and gene sequencing. I propose an algorithm that enables the robot to plan an optimal path from an initial position to a specific goal with a free collision with obstacles and other moving robots. Based on artificial intelligence techniques like A* to find an optimal path for each robot while cooperating with other robots. The optimality of the path can be measured using an objective function that considers the shortest distance, and/or the least time required. The information about the environment is known previously and obstacles are static. Finally, the proposed algorithm results are introduced with many examples and cases. Keywords: Path Planning, Optimal Path, Robot, Obstacles, Static Environment, A* Algorithm, Grid Map. 1. INTRODUCTION Path-planning is important for autonomous mobile robots that let robots find the optimal path between two points. The main task of the path planning is to make the robot move to the goal without collision with obstacles and other robots. This problem is one of the problems in artificial intelligence. The goal is to make multiple mobile robots system cooperate with each other with free-collision, avoiding obstacles and avoid interference with each other [1]. Path planning is important to find the shortest path to the goal and it needs a map of the environment [2]. The optimality of the path can be measured using an objective function that considers the shortest distance, and/or the least time required. Some of the project applications are ''search and rescue, planetary exploration, mineral mining, transportation, agriculture, industrial maintenance, security, surveillance and warehouse management'' [1]. Based on A* algorithm we introduce a method to solve the on-line path planning problem by considering obstacles and free collision with other robots. Different approaches have been presented for implementing path planning for multiple mobile robot system since multiple robot can improve the working capability and performance [3]. One of the approaches for on-line path planning of multiple mobile robot system is efficient artificial bee colony (EABC) algorithm. The environment is known. "The proposed EABC algorithm enhances the performance by using best individuals for preserving good evolution, the solution provides a proper direction for searching, the update strategy provides the newest information of solution" [3]. Each situation design path planning for next position. First, separate each objective function to decide each one from its own perspective. Then, combine path planning for next position and so on. Thus, the next position of each robot is designed. Now the mobiles robots can move to the goal without collision [3]. Another approach deals with path planning based on grid map using modifications and improvements on A* algorithm. "The basic method assumption is a functional and reliable reactive navigation and SLAM" (Simultaneous Localization and Mapping) [5]. The scientific paper uses basic A* algorithm. Basic A* algorithm used for a grid configuration space, but this algorithm is not quite useful because there can be a lot of free space between the connected squares over long distances and these squares may not be linked next each other. Therefore, the searching in every angle is introduced. The algorithm uses searching in angles which called Theta* and Phi*. Basic Theta* and Phi* are modifications of A* algorithm. These modifications focus on chose the optimal path at least time. The article proposes that the algorithms are suitable in some cases while other algorithms are suitable in other cases [5]. Last approach we will talk about for autonomous mobile robot in un-known environment. The paper introduced singleton type fuzzy logic system controller and Fuzzy- WDO hybrid. The WDO (Wind Driven Optimization) algorithm is used to optimized and set the previous information and result