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.


    Access

    Download


    Export, share and cite



    Title :

    Particle Swarm Optimization—An Adaptation for the Control of Robotic Swarms


    Contributors:

    Publication date :

    2021-06-30


    Remarks:

    Rossides , G , Metcalfe , B & Hunter , A 2021 , ' Particle Swarm Optimization—An Adaptation for the Control of Robotic Swarms ' , Robotics , vol. 10 , no. 2 , 58 . https://doi.org/10.3390/robotics10020058



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629



    IoD swarms collision avoidance via improved particle swarm optimization

    Ahmed, Gamil / Sheltami, Tarek / Mahmoud, Ashraf et al. | Elsevier | 2020


    Optimizing Laying Hen Diet Using Particle Swarm Optimization with Two Swarms

    Alfarisy, Gusti Ahmad Fanshuri / Mahmudy, Wayan Firdaus / Natsir, Muhammad Halim | BASE | 2018

    Free access

    Particle swarm optimization for GPS navigation Kalman filter adaptation

    Jwo, Dah-Jing / Chang, Shun-Chieh | Emerald Group Publishing | 2009


    Scalable Human Interaction with Robotic Swarms

    Pendleton, Brian / Goodrich, Michael | AIAA | 2013