| 蚁群-势场算法在水下重力辅助导航航迹规划中的应用 |
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| 引用本文： | 张驰,李姗姗,史颜俊,邢志斌,范雕.蚁群-势场算法在水下重力辅助导航航迹规划中的应用[J].测绘学报,2020,49(7):865-873. |
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| 作者姓名： | 张驰 李姗姗 史颜俊 邢志斌 范雕 |
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| 作者单位： | 1. 信息工程大学, 河南 郑州 450001;2. 92337部队, 辽宁 大连 116023 |
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| 基金项目： | 国家自然科学基金（41777021；41574020）；信息工程大学校立课题（2017503902） |
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| 摘 要： | 水下潜器航迹处于重力特征变化明显的适配区域才能保证重力辅助导航的有效实施,因此在重力匹配导航阶段,潜器的航迹规划至关重要。本文首先依据重力统计特征参数对水下潜器航行区域进行适配性划分,并给出适配、非适配区标签;然后在蚁群算法进行航迹规划的基础上引入人工势场算法,重新构建启发函数,避免了蚁群算法的局部最优问题;同时利用最大-最小蚁群系统改进算法信息素更新规则,防止了"早熟"现象发生。仿真试验结果表明,本文提出的蚁群-势场算法可以有效解决水下潜器在重力辅助导航中的航迹优化问题,提高了问题解的可行性。
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| 关 键 词： | 航迹规划 蚁群算法 人工势场法 重力辅助导航 |
| 收稿时间： | 2019-05-16 |
| 修稿时间： | 2019-11-27 |
Application of ant colony-potential field algorithm in underwater gravity matching navigation track planning |
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| ZHANG Chi,LI Shanshan,SHI Yanjun,XING Zhibin,FAN Diao.Application of ant colony-potential field algorithm in underwater gravity matching navigation track planning[J].Acta Geodaetica et Cartographica Sinica,2020,49(7):865-873. |
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| Authors: | ZHANG Chi LI Shanshan SHI Yanjun XING Zhibin FAN Diao |
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| Institution: | 1. Information Engineering University, Zhengzhou 450001, China;2. Troops 92337, Dalian 116023, Chinat |
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| Abstract: | The underwater submersible track in the adaptation area where the gravity characteristics change obviously can ensure the effective implementation of gravity-assisted navigation. Therefore, in the gravity matching navigation stage, the path planning of the submersible is very important. In this paper, the adaptive submarine navigation area is adaptively divided according to the gravity statistical characteristic parameters, and the adaptive and non-adaptive area labels are given. Then the artificial potential field algorithm is introduced based on the ant colony algorithm for the path planning. Reconstructing the heuristic function avoids the local optimal problem of the ant colony algorithm. At the same time, the maximum-minimum ant colony system is used to improve the pheromone update rule, which prevents the “premature” phenomenon. The simulation results show that the proposed ant colony-potential field algorithm can effectively solve the path optimization problem of underwater submersible in gravity-assisted navigation and improve the feasibility of the solution. |
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