• DocumentCode
    578189
  • Title

    PSO-based parameters optimization of multi-robot formation navigation in unknown environment

  • Author

    Liu, Qiang ; Ma, Jiachen ; Zhang, Qi

  • Author_Institution
    Sch. of Astronaut., Harbin Inst. of Technol., Harbin, China
  • fYear
    2012
  • fDate
    6-8 July 2012
  • Firstpage
    3571
  • Lastpage
    3576
  • Abstract
    This paper proposed a PSO-based algorithm for parameters optimization of multi-robot formation navigation in unknown environment. In order to achieve formation navigation in unknown environment, each robot in formation adopts motor schema-based reactive control architecture which has four primitive behaviors called move_to_goal, keep_formation, avoid_static_obstacle and avoid_robot behaviors.The behavior output to direct the movement of robot is made by the combination of four primitive behaviors. Particle Swarm Optimization algorithm as an unsupervised learning method for a reactive control architecture greatly reduces the effort required to configure reactive control parameters of multi-robot formation system. The validity of this method is verified through computer simulations in different types of in environments by robot simulation software MissionLab.
  • Keywords
    control engineering computing; multi-robot systems; navigation; particle swarm optimisation; unsupervised learning; MissionLab; PSO; avoid_robot behavior; avoid_static_obstacle behavior; computer simulation; keep_formation behavior; motor schema-based reactive control architecture; move_to_goal, behavior; multirobot formation navigation; parameter optimization; particle swarm optimization; robot simulation software; unsupervised learning method; Collision avoidance; Diamond-like carbon; Navigation; Robot kinematics; Trajectory; Vectors; MissionLab; PSO; formation; motor schema; multi-robot; reactive control;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Control and Automation (WCICA), 2012 10th World Congress on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-1-4673-1397-1
  • Type

    conf

  • DOI
    10.1109/WCICA.2012.6359066
  • Filename
    6359066