Skip to main content

Exploratory Path Planning for Mobile Robots in Dynamic Environments with Ant Colony Optimization

Santos, Valeria, Otero, Fernando E.B., Johnson, Colin G., Osorio, Fernando, Toledo, Claudio (2020) Exploratory Path Planning for Mobile Robots in Dynamic Environments with Ant Colony Optimization. In: Genetic and Evolutionary Computation Conference (GECCO ’20), 8–12 July 2020, Cancun, Mexico. (In press) (doi:10.1145/3377930.3390219) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:81040)

PDF Author's Accepted Manuscript
Language: English

Restricted to Repository staff only until 8 July 2020.

Creative Commons Licence
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Contact us about this Publication
Official URL


In the path planning task for autonomous mobile robots, robots should be able to plan their trajectory to leave the start position and reach the goal, safely. There are several path planning approaches for mobile robots in the literature. Ant Colony Optimization algorithms have been investigated for this problem, giving promising results. In this paper, we propose the Max-Min Ant System for Dynamic Path Planning algorithm for the exploratory path planning task for autonomous mobile robots based on topological maps. A topological map is an environment representation whose focus is the main reference points of the environment and their connections. Based on this representation, the path can be composed by a sequence of state/actions pairs, which facilitates the navigability of the path, with no need to have the information of the complete map. The proposed algorithm was evaluated in static and dynamic envi- ronments, showing promising results in both of them. Experiments in dynamic environments show the adaptability of our proposal.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1145/3377930.3390219
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Faculties > Sciences > School of Computing > Data Science
Depositing User: Fernando Otero
Date Deposited: 28 Apr 2020 16:12 UTC
Last Modified: 29 Apr 2020 13:07 UTC
Resource URI: (The current URI for this page, for reference purposes)
Otero, Fernando E.B.:
Johnson, Colin G.:
  • Depositors only (login required):


Downloads per month over past year