Recently, research on path planning for the autonomous underwater vehicles (AUVs) has developed rapidly. Heuristic algorithms have been widely used to plan a path for AUV, but most traditional heuristic algorithms are facing two problems, one is slow convergence speed, the other is premature convergence. To solve the above problems, this paper proposes a new heuristic algorithms fusion, which improves the genetic algorithm with the ant colony optimization algorithm and the simulated annealing algorithm. In addition, to accelerate convergence and expand the search space of the algorithm, some algorithms like trying to cross, path self-smoothing and probability of genetic operation adjust adaptively are proposed. The advantages of the proposed algorithm are reflected through simulated comparative experiments. Besides, this paper proposes an ocean current model and a kinematics model to solve the problem of AUV path planning under the influence of ocean currents.

中文翻译：

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基于融合启发式算法的洋流影响下自主水下航行器路径规划


近年来，自主水下航行器（AUV）路径规划的研究发展迅速。启发式算法已广泛用于AUV路径规划，但大多数传统启发式算法面临两个问题，一是收敛速度慢，二是早熟收敛。针对上述问题，本文提出一种新的启发式融合算法，用蚁群优化算法和模拟退火算法对遗传算法进行改进。此外，为了加速收敛，扩大算法的搜索空间，提出了尝试交叉、路径自平滑、遗传操作概率自适应调整等算法。通过模拟对比实验体现了该算法的优点。此外，本文提出了洋流模型和运动学模型来解决洋流影响下AUV路径规划问题。