Title :
Research on cluster attack mission planning of multi USVs based on DAM-BBOPSO algorithm
Author :
Li Jie ; Sun Yao ; Zhenxing Zhang
Author_Institution :
Coll. of Autom., Harbin Eng. Univ., Harbin, China
Abstract :
The rapid development of modern defense technology decreased the USVs´ attacking effect greatly, autonomous formation cluster attack techniques of USVs has became one of the key technologies of future naval warfare, mission planning among USVs is the key for them to complete tasks smoothly and efficiently. Regarding the cluster attack mission planning problem as multi-constrained task allocation process, building mission planning model, and improve particle initialization and optimization process combined with distributed auction mechanism(DAM) and Biogeography-Based Optimization algorithm(BBO). Simulation result indicates that the program achieved with distributed auction mechanism particle swarm optimization could fully meet the requirements of USVs´ cluster attack missions, and shows better convergence compared with traditional particle swarm optimization(PSO) and other swarm intelligence algorithms.
Keywords :
military vehicles; mobile robots; multi-robot systems; particle swarm optimisation; ships; swarm intelligence; DAM-BBOPSO algorithm; USV attacking effect; USV cluster attack mission planning problem; autonomous formation cluster attack techniques; biogeography-based optimization algorithm; defense technology; distributed auction mechanism; distributed auction mechanism particle swarm optimization; mission planning model; multiUSV; multiconstrained task allocation process; naval warfare; particle initialization process; particle optimization process; swarm intelligence algorithms; Clustering algorithms; Marine vehicles; Optimization; Particle swarm optimization; Planning; Sociology; Statistics; BBOPSO; Distributed auction mechanism; Mission planning; Multi USVs;
Conference_Titel :
Mechatronics and Automation (ICMA), 2013 IEEE International Conference on
Conference_Location :
Takamatsu
Print_ISBN :
978-1-4673-5557-5
DOI :
10.1109/ICMA.2013.6618161