This paper is published in Volume-4, Issue-3, 2018
Area
Artificial Intellegence
Author
Mohammed Habeeb Vulla
Org/Univ
Dravidian University, Kuppam, Andhra Pradesh, India
Keywords
Path Planning, Optimal Path, Robot, Obstacles, Static Environment, A* Algorithm, Grid Map.
Citations
IEEE
Mohammed Habeeb Vulla. Autonomous robot navigation in obstacles based environment, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.
APA
Mohammed Habeeb Vulla (2018). Autonomous robot navigation in obstacles based environment. International Journal of Advance Research, Ideas and Innovations in Technology, 4(3) www.IJARIIT.com.
MLA
Mohammed Habeeb Vulla. "Autonomous robot navigation in obstacles based environment." International Journal of Advance Research, Ideas and Innovations in Technology 4.3 (2018). www.IJARIIT.com.
Mohammed Habeeb Vulla. Autonomous robot navigation in obstacles based environment, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.
APA
Mohammed Habeeb Vulla (2018). Autonomous robot navigation in obstacles based environment. International Journal of Advance Research, Ideas and Innovations in Technology, 4(3) www.IJARIIT.com.
MLA
Mohammed Habeeb Vulla. "Autonomous robot navigation in obstacles based environment." International Journal of Advance Research, Ideas and Innovations in Technology 4.3 (2018). www.IJARIIT.com.
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 the 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.