Repository logo

Infoscience

  • English
  • French
Log In
Logo EPFL, École polytechnique fédérale de Lausanne

Infoscience

  • English
  • French
Log In
  1. Home
  2. Academic and Research Output
  3. Conferences, Workshops, Symposiums, and Seminars
  4. Distributed Particle Swarm Optimization for Limited Time Adaptation in Autonomous Robots
 
conference paper

Distributed Particle Swarm Optimization for Limited Time Adaptation in Autonomous Robots

Di Mario, Ezequiel  
•
Martinoli, Alcherio  
2014
Distributed Autonomous Robotic Systems
International Symposium on Distributed Autonomous Robotic Systems

Evaluative techniques offer a tremendous potential for on-line controller design. However, when the optimization space is large and the performance metric is noisy, the time needed to properly evaluate candidate solutions becomes prohibitively large and, as a consequence, the overall adaptation process becomes extremely time consuming. Distributing the adaptation process reduces the required time and increases robustness to failure of individual agents. In this paper, we analyze the role of the four algorithmic parameters that determine the total evaluation time in a distributed implementation of a Particle Swarm Optimization algorithm. For a multi-robot obstacle avoidance case study, we explore in simulation the lower boundaries of these parameters with the goal of reducing the total evaluation time so that it is feasible to implement the adaptation process within a limited amount of time determined by the robots' energy autonomy. We show that each parameter has a different impact on the final fitness and propose some guidelines for choosing these parameters for real robot implementations.

  • Files
  • Details
  • Metrics
Loading...
Thumbnail Image
Name

dars2012edm.pdf

Type

Preprint

Version

Submitted version (Preprint)

Access type

openaccess

Size

176.41 KB

Format

Adobe PDF

Checksum (MD5)

3d73243c142deda62526c97400ddd286

Logo EPFL, École polytechnique fédérale de Lausanne
  • Contact
  • infoscience@epfl.ch

  • Follow us on Facebook
  • Follow us on Instagram
  • Follow us on LinkedIn
  • Follow us on X
  • Follow us on Youtube
AccessibilityLegal noticePrivacy policyCookie settingsEnd User AgreementGet helpFeedback

Infoscience is a service managed and provided by the Library and IT Services of EPFL. © EPFL, tous droits réservés