针对高密度复杂环境下的无人水面航行器（USV）航迹规划问题，将A*算法和蚁群算法相结合，提出一种改进型A*-蚁群混合算法。本算法结合A*算法在低密度环境区域航路规划的优势性，同时，当遇到高密度环境区域时引入蚁群算法提高局部规划能力，在传统蚁群算法基础上，改进了信息素的更新模型，增强了可行路径中最优路径的信息浓度，减弱了最差路径的信息浓度，并通过调整信息素浓度总和比例，增强算法的寻优能力。该方法能够有效地平衡全局和局部规划，提高在复杂环境下的USV航迹规划能力。通过仿真，验证了在复杂环境下该算法的有效性和优越性。
In order to solve the problem of path planning about unmanned surface vehicle(USV) in high-density and complex environment, an improved A*-ant hybrid algorithm is proposed by combining A * algorithm with ant colony algorithm. This algorithm combines the advantages of A * algorithm in route planning in low-density environment. At the same time, the ant colony algorithm is introduced to improve the ability of local planning when encountering high-density environment. Based on the traditional ant colony algorithm, the updating model of pheromone is improved, the information concentration of the best path in the feasible path is enhanced, and the information concentration of the worst path is weakened. By adjusting the proportion of the total pheromone concentration, the optimization ability of the algorithm is enhanced. This method can effectively balance the global and local planning, and improve the track planning ability in complex environment. The simulation results show that the algorithm is effective and superior in complex environment.
2021,43(8): 83-87 收稿日期：2020-08-18
DOI：10.3404/j.issn.1672-7649.2021.08.016
分类号：E925
作者简介：孙海文,男,博士,助理研究员,主要研究方向为武器系统建模与仿真、舰空导弹综合控制技术
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