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Application of GA, PSO, and ACO Algorithms to Path Planning of Autonomous Underwater Vehicles 被引量:8

Application of GA, PSO, and ACO Algorithms to Path Planning of Autonomous Underwater Vehicles
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摘要 In this paper, an underwater vehicle was modeled with six dimensional nonlinear equations of motion, controlled by DC motors in all degrees of freedom. Near-optimal trajectories in an energetic environment for underwater vehicles were computed using a nnmerical solution of a nonlinear optimal control problem (NOCP). An energy performance index as a cost function, which should be minimized, was defmed. The resulting problem was a two-point boundary value problem (TPBVP). A genetic algorithm (GA), particle swarm optimization (PSO), and ant colony optimization (ACO) algorithms were applied to solve the resulting TPBVP. Applying an Euler-Lagrange equation to the NOCP, a conjugate gradient penalty method was also adopted to solve the TPBVP. The problem of energetic environments, involving some energy sources, was discussed. Some near-optimal paths were found using a GA, PSO, and ACO algorithms. Finally, the problem of collision avoidance in an energetic environment was also taken into account.
出处 《Journal of Marine Science and Application》 2012年第3期378-386,共9页 船舶与海洋工程学报(英文版)
关键词 path planning autonomous underwater vehicle genetic algorithm (GA) particle swarmoptimization (PSO) ant colony optimization (ACO) collision avoidance 自主水下航行器 遗传算法 PSO ACO GA 路径规划 应用 两点边值问题
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