Particle Swarm Optimization (PSO) is a numerical optimization technique based on the motion of virtual particles within a multidimensional space. The particles explore the space in an attempt to find minima or maxima to the optimization problem. The motion of the particles is linked, and the overall behavior of the particle swarm is controlled by several parameters. PSO has been proposed as a control strategy for physical swarms of robots that are localizing a source; the robots are analogous to the virtual particles. However, previous attempts to achieve this have shown that there are inherent problems. This paper addresses these problems by introducing a modified version of PSO, as well as introducing new guidelines for parameter selection. The proposed algorithm links the parameters to the velocity and acceleration of each robot, and demonstrates obstacle avoidance. Simulation results from both MATLAB and Gazebo show close agreement and demonstrate that the proposed algorithm is capable of effective control of a robotic swarm and obstacle avoidance.

Original languageEnglish
Article number58
Issue number2
Early online date8 Apr 2021
Publication statusPublished - 30 Jun 2021

Bibliographical note

Funding Information:
Funding: This research and the APC were funded by both the UK Natural Environment Research Council (NERC) and Engineering and Physical Sciences Research Council (EPSRC) grant number NE/N012070/1.


  • Obstacle avoidance
  • Particle swarm
  • PSO
  • Swarm robotics

ASJC Scopus subject areas

  • Mechanical Engineering
  • Control and Optimization
  • Artificial Intelligence


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