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http://dx.doi.org/10.12989/arr.2018.2.2.161

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)
Publication Information
Advances in robotics research / v.2, no.2, 2018 , pp. 161-182 More about this Journal
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.
Keywords
formation control; receding horizon control; sequential Monte Carlo; unmanned surface vehicle; collision avoidance;
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Times Cited By KSCI : 2  (Citation Analysis)
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