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

Journal: Sensors (Basel, Switzerland)
Published Date: Mar 28, 2020

Abstract

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 modified aging ant colony optimization (AACO). The AACO 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.

Authors

  • Fatin Hassan Ajeil
    Department of Electrical Engineering, College of Engineering, University of Baghdad, Al-Jadriyah, Baghdad 10001, Iraq.
  • Ibraheem Kasim Ibraheem
    Department of Electrical Engineering, College of Engineering, University of Baghdad, Al-Jadriyah, Baghdad 10001, Iraq.
  • Ahmad Taher Azar
    Faculty of Computers and Information, Benha University, Egypt. Electronic address: ahmad_t_azar@ieee.org.
  • Amjad J Humaidi
    Department of Control and Systems Engineering, University of Technology, Baghdad 10001, Iraq.

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