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
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Htun Myint, Department of Electronic Engineering, Technological University (Panglong), Panglong, Myanmar
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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.
Collision Free Path, Robot Navigation, MATLAB, A Star, Path Planning Algorithm
To cite this article
Htun Myint, Development of Robot Navigation System with Collision Free Path Planning Algorithm, Machine Learning Research. Vol. 3, No. 3, 2018, pp. 60-68. doi: 10.11648/j.mlr.20180303.12
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
RAFIA INAM, “A* Algorithm for Multicore Graphics Processors”, Department of Computer Science and Engineering Division of Computer Engineering, CHALMERS UNIVERSITY OF TECHNOLOGY, 2009.
P.E. Hart, N.J.Nilsson, and B. Raphael, "A formal basis for the heuristic determination of minimum cost paths". IEEE Transactions on System Science and Cybemetics. 4 pp 100-107, 1968.
Rina Dechter and Judea Pearl, “Generalized best-first search strategies and the optimality of A*”, Journal of The ACM, Volume 32, Issue 3, Pages: 505 - 536, 1985.
R. E. Korf, “Depth First Iterative Deeping: An Optimal Admissible Tree Search”, Journal of Artificial Intelligence, pp. 97-100, 1985.
R. E. Korf, “Real-time Heuristic Search”. Artificial Intelligence, 42(2- 3), pp. 189-211, 1990.
R. Holte, M. Perez, R. Zimmer, and A. MacDonald. “Hierarchical A*: Searching Abstraction Hierarchies Efficiently”. In Proceedings AAAI-96, pp. 530-535, 1996.
R. Holte, T. Mkadmi, R. Zimmer, and A. MacDonald. “Speeding Up Problem-Solving by Abstraction: A Graph Oriented Approach”. Artificial Intelligence Journal, 85(1-2), pp. 321-361, 1996.
Samuel Grant Dawson Williams, “Using the A-Star Path-Finding Algorithm for Solving General and Constrained Inverse Kinematics Problems”, Japan, 2008.
Mohd Azlan Shah Abd Rahim and Illani Mohd Nawi, “Path Planning Automated Guided Robot”, Proceedings of the World Congress on Engineering and Computer Science 2008 WCECS 2008, October 22 - 24, 2008, San Francisco, USA, 2008.
Bruno Siciliano, Lorenzo Sciavicco, Luigi Villani, and Giuseppe Oriolo, “Robotics Modelling, Planning and Control”, Spain, 2009.
Giacomo Nannicini, “Point-to-Point Shortest Paths on Dynamic Time-Dependent Road Networks”, French, 2009.
Rehman Tariq Butt, “Performance Comparison of AI Algorithms Anytime Algorithms”, Sweden, 2008.
Muntasir Raihan Rahman, “On-Line Algorithms for Rankings of Graphs”, Bangladesh, 2006.
Subramanian MB, Sudhagar K, Rajarajeswari G. Design of navigation control architecture for an autonomous mobile robot agent. Indian Journal of Science and Technology. 2016 Mar; 9(10). Doi no: 10.17485/ijst/2016/v9i10/85769.
Prasad KM, Reddy ARM, Rao KV. Anomaly based Real Time Prevention of under rated App-DDOS attacks on web: An experiential metrics based machine learning approach. Indian Journal of Science and Technology. 2016 Jul; 9(27). Doi no: 10.17485/ijst/2016/v9i27/87872.
Murikipudi A, Prakash V, Vigneswaran T. Performance analysis of real time operating system with general purpose operating system for mobile robotic system. Indian Journal of Science and Technology. 2015 Aug; 8(18). Doi no:10.17485/ijst/2015/v8i19/77017.
Suresh KS, Vaithiyanathan V, Venugopal S. Layered approach for three dimensional collision free robot path planning using genetic algorithm. Indian Journal of Science and Technology. 2015 Dec; 8(35). Doi no:10.17485/ijst/2015/v8i35/86639.
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