Particle Swarm Optimization—An Adaptation for the Control of Robotic Swarms
| Autor: | Alan J. Hunter, Benjamin Metcalfe, George Rossides |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Optimization problem Control and Optimization Swarm robotics Computer science lcsh:Mechanical engineering and machinery Computer Science::Neural and Evolutionary Computation MathematicsofComputing_NUMERICALANALYSIS 02 engineering and technology ComputingMethodologies_ARTIFICIALINTELLIGENCE Computer Science::Robotics Acceleration obstacle avoidance 020901 industrial engineering & automation Particle swarm Control theory Artificial Intelligence Obstacle avoidance 0202 electrical engineering electronic engineering information engineering lcsh:TJ1-1570 swarm robotics Mechanical Engineering ComputingMethodologies_MISCELLANEOUS PSO Swarm behaviour Particle swarm optimization particle swarm Maxima and minima Robot 020201 artificial intelligence & image processing |
| Zdroj: | 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 Robotics, Vol 10, Iss 58, p 58 (2021) Robotics Volume 10 Issue 2 |
| DOI: | 10.3390/robotics10020058 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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