Development of Robot Navigation System with Collision Free Path Planning Algorithm
Machine Learning Research
Volume 3, Issue 3, September 2018, Pages: 60-68
Received: Aug. 3, 2018;
Accepted: Sep. 6, 2018;
Published: Oct. 12, 2018
Views 971 Downloads 142
Htun Myint, Department of Electronic Engineering, Technological University (Panglong), Panglong, Myanmar
Mobile robots have been successfully used in many fields due to their abilities to perform difficult tasks in hazardous environments, such as robot rescuing, space exploring and their various promising applications in the daily lives. Robot path planning is a key issue in robot navigation which is a kernel part in mobile robot technology. Robot path planning is to generate a collision-free path in an environment while satisfying some optimization criteria. Mobile robot path planning is a nondeterministic polynomial time (NP) problem, traditional optimization methods are not very effective to it, which are easy to plunge into local minimum. In this research work, an evolutionary algorithm to solve the robot path planning problem is devised. A method of robot path planning in partially unknown environments based on A star (A*) algorithm was proposed. The proposed algorithm allows a mobile robot to navigate through static obstacles and finds its path in order to reach from its initial position to the target without collision. In addition, the environment is partially unknown for the robot due to the limit detection range of its sensors. The robot processor updates its information during the motion. The simulations are performed in different static environments, and the results show that the robot reaches its target with colliding free obstacles. The optimal path is generated with this method when the robot reaches its target. The simulation results are developed by MATLAB environments.
Development of Robot Navigation System with Collision Free Path Planning Algorithm, Machine Learning Research.
Vol. 3, No. 3,
2018, pp. 60-68.
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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