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Open AccessArticle

Grid-Based Mobile Robot Path Planning Using Aging-Based Ant Colony Optimization Algorithm in Static and Dynamic Environments

1
Department of Electrical Engineering, College of Engineering, University of Baghdad, Baghdad 10001, Iraq
2
Robotics and Internet-of-Things Lab (RIOTU), Prince Sultan University, Riyadh 11586, Saudi Arabia
3
Faculty of Computers and Artificial Intelligence, Benha University, Benha 13518, Egypt
4
Department of Control and Systems Engineering, University of Technology, Baghdad 10001, Iraq
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(7), 1880; https://doi.org/10.3390/s20071880
Received: 7 March 2020 / Revised: 25 March 2020 / Accepted: 26 March 2020 / Published: 28 March 2020
(This article belongs to the Special Issue Sensors and Robot Control)
Planning an optimal path for a mobile robot is a complicated problem as it allows the mobile robots to navigate autonomously by following the safest and shortest path between starting and goal points. The present work deals with the design of intelligent path planning algorithms for a mobile robot in static and dynamic environments based on swarm intelligence optimization. A modification based on the age of the ant is introduced to standard ant colony optimization, called aging-based ant colony optimization (ABACO). The ABACO was implemented in association with grid-based modeling for the static and dynamic environments to solve the path planning problem. The simulations are run in the MATLAB environment to test the validity of the proposed algorithms. Simulations showed that the proposed path planning algorithms result in superior performance by finding the shortest and the most free-collision path under various static and dynamic scenarios. Furthermore, the superiority of the proposed algorithms was proved through comparisons with other traditional path planning algorithms with different static environments. View Full-Text
Keywords: mobile robot; path planning; aging-based ant colony optimization (ABACO); dynamic environment; grid-based modeling mobile robot; path planning; aging-based ant colony optimization (ABACO); dynamic environment; grid-based modeling
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MDPI and ACS Style

Ajeil, F.H.; Ibraheem, I.K.; Azar, A.T.; Humaidi, A.J. Grid-Based Mobile Robot Path Planning Using Aging-Based Ant Colony Optimization Algorithm in Static and Dynamic Environments. Sensors 2020, 20, 1880.

AMA Style

Ajeil FH, Ibraheem IK, Azar AT, Humaidi AJ. Grid-Based Mobile Robot Path Planning Using Aging-Based Ant Colony Optimization Algorithm in Static and Dynamic Environments. Sensors. 2020; 20(7):1880.

Chicago/Turabian Style

Ajeil, Fatin H.; Ibraheem, Ibraheem K.; Azar, Ahmad T.; Humaidi, Amjad J. 2020. "Grid-Based Mobile Robot Path Planning Using Aging-Based Ant Colony Optimization Algorithm in Static and Dynamic Environments" Sensors 20, no. 7: 1880.

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