<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The autonomous navigation of robots in unknown environments is a challenge since it needs the integration of a several subsystems to implement different functionality. It needs drawing a map of the environment, robot map localization, motion planning or path following, implementing the path in real-world, and many others; all have to be implemented simultaneously. Thus, the development of autonomous robot navigation (ARN) problem is essential for the growth of the robotics field of research. In this paper, we present a simulation of a swarm intelligence method is known as Particle Swarm Optimization (PSO) to develop an ARN system that can navigate in an unknown environment, reaching a pre-defined goal and become collision-free. The proposed system is built such that each subsystem manipulates a specific task which integrated to achieve the robot mission. PSO is used to optimize the robot path by providing several waypoints that minimize the robot traveling distance. The <italic>Gazebo</italic> simulator was used to test the response of the system under various envirvector representing a solution to the optimization problem.onmental conditions. The proposed ARN system maintained robust navigation and avoided the obstacles in different unknown environments. vector representing a solution to the optimization problem.</p></abstract>

The autonomous navigation of robots in unknown environments is a challenge since it needs the integration of a several subsystems to implement different functionality. It needs drawing a map of the environment, robot map localization, motion planning or path following, implementing the path in real-world, and many others; all have to be implemented simultaneously. Thus, the development of autonomous robot navigation (ARN) problem is essential for the growth of the robotics field of research. In this paper, we present a simulation of a swarm intelligence method is known as Particle Swarm Optimization (PSO) to develop an ARN system that can navigate in an unknown environment, reaching a pre-defined goal and become collision-free. The proposed system is built such that each subsystem manipulates a specific task which integrated to achieve the robot mission. PSO is used to optimize the robot path by providing several waypoints that minimize the robot traveling distance. The Gazebo simulator was used to test the response of the system under various envirvector representing a solution to the optimization problem.onmental conditions. The proposed ARN system maintained robust navigation and avoided the obstacles in different unknown environments. vector representing a solution to the optimization problem.

Collision-Free Autonomous Robot Navigation in Unknown Environments Utilizing PSO for Path Planning

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

The autonomous navigation of robots in unknown environments is a challenge since it needs the integration of a several subsystems to implement different...

Collision-Free Autonomous Robot Navigation in Unknown Environments Utilizing PSO for Path Planning

Publié en ligne: 30 août 2019

Pages: 267 - 282

Reçu: 17 juin 2018

Accepté: 12 mai 2019

DOI: https://doi.org/10.2478/jaiscr-2019-0008

Mots clés
mobile robot, particle swarm optimization, path planning

© 2019 Evan Krell et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Collision-Free Autonomous Robot Navigation in Unknown Environments Utilizing PSO for Path Planning

Publié en ligne: 30 août 2019

Pages: 267 - 282

Reçu: 17 juin 2018

Accepté: 12 mai 2019

DOI: https://doi.org/10.2478/jaiscr-2019-0008

Mots clésmobile robot, particle swarm optimization, path planning

© 2019 Evan Krell et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Mots clés
mobile robot, particle swarm optimization, path planning