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Particle swarm optimization-based receding horizon formation control of multi-agent surface vehicles

  • Kim, Donghoon (Seadronix) ;
  • Lee, Seung-Mok (Department of Mechanical and Automotive Engineering, Keimyung University) ;
  • Jung, Sungwook (Urban Robotics Lab., Department of Civil Engineering, Korea Advanced Institute for Science and Technology) ;
  • Koo, Jungmo (Urban Robotics Lab., Department of Civil Engineering, Korea Advanced Institute for Science and Technology) ;
  • Myung, Hyun (Urban Robotics Lab., Department of Civil Engineering, Korea Advanced Institute for Science and Technology)
  • Received : 2018.06.13
  • Accepted : 2018.06.18
  • Published : 2018.06.25

Abstract

This paper proposes a novel receding horizon control (RHC) algorithm for formation control of a swarm of unmanned surface vehicles (USVs) using particle swarm optimization (PSO). The proposed control algorithm provides the coordinated path tracking of multi-agent USVs while preventing collisions and considering external disturbances such as ocean currents. A three degrees-of-freedom kinematic model of the USV is used for the RHC with guaranteed stability and convergence by incorporating a sequential Monte Carlo (SMC)-based particle initialization. An ocean current model-based estimator is designed to compensate for the effect of ocean currents on the USVs. This method is compared with the PSO-based RHC algorithms to demonstrate the performance of the formation control and the collision avoidance in the presence of ocean currents through numerical simulations.

Acknowledgement

Supported by : National Research Foundation of Korea (NRF)

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