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Advances in Robotics Research   Volume 2, Number 2, June 2018, pages 161-182
DOI: http://dx.doi.org/10.12989/arr.2018.2.2.161
 
Particle swarm optimization-based receding horizon formation control of multi-agent surface vehicles
Donghoon Kim, Seung-Mok Lee, Sungwook Jung, Jungmo Koo and Hyun Myung

 
Abstract     [Full Text]
    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.
 
Key Words
    formation control; receding horizon control; sequential Monte Carlo; unmanned surface vehicle; collision avoidance
 
Address
Donghoon Kim: Seadronix, 193 Muji-ro, Yuseong-gu, Daejeon 34051, Republic of Korea

Seung-Mok Lee: Department of Mechanical and Automotive Engineering, Keimyung University, 1095 Dalgubeol-daero,
Dalseo-Gu, Daegu 42601, Republic of Korea

Sungwook Jung, Jungmo Koo and Hyun Myung: Urban Robotics Lab., Department of Civil Engineering, Korea Advanced Institute for Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
 

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