多域限界内多AUV巡逻航路规划法

严浙平; 何靓文; 李 娟

doi:10.11993/j.issn.2096-3920.2017.03.004

多域限界内多AUV巡逻航路规划法

doi: 10.11993/j.issn.2096-3920.2017.03.004

哈尔滨工程大学, 黑龙江哈尔滨, 150001

基金项目: 国家自然科学基金项目资助(51679057); 黑龙江省杰出青年科学基金项目资助(JC2016007); 中央高校基本科研业务费专项基金资助(HEUCFM170403)

详细信息

作者简介:
严浙平(1972-), 男, 工学博士, 教授, 研究方向为水下无人航行器的总体设计、智能控制和运动控制.

中图分类号: TP24; U612.1
计量
- 文章访问数: 1125
- HTML全文浏览量: 1
- PDF下载量: 329
- 被引次数: 0
出版历程
- 收稿日期: 2017-03-31
- 修回日期: 2017-04-26
- 刊出日期: 2017-08-20

Path Planning Method for Multi-AUVs Patrol in Restricted Multizone Area

College of Automation, Harbin Engineering University, Harbin 150001, China

摘要

摘要: 传统粒子群优化算法在应用于复杂环境自主式水下航行器(AUV)协同航路规划时, 由于粒子群更新过程中缺少约束性, 极易产生不可行路径。针对该问题, 文中提出了一种适用于复杂环境的多AUV协同航路规划优化算法。该方法将预测控制与粒子群算法相结合, 将两步预测植入粒子更新过程, 对更新的粒子进行检测, 避免了不可行粒子的生成。文中基于栅格法对环境进行建模, 将环境的覆盖信息、不确定度等存入矢量栅格中。将文中改进的算法以岛礁监视为应用背景进行了仿真验证, 结果表明, 该算法实现了在复杂环境中的多AUV巡逻航路规划, 且对于不同性质区域具有不同的巡逻频率, 具有较好的巡逻效能, 更符合实际的应用环境。
- 多AUV /
- 协同路径规划 /
- 预测控制 /
- 粒子群算法
Abstract: Conventional particle swarm optimization(PSO) may generate infeasible path due to lack of constraints during the particle swarm updating process when it is applied to cooperative path planning for autonomous undersea vehicle(AUV) in complex environment. In this paper, a cooperative path planning optimization method for multi-AUVs in complex environments is proposed. This method combines the predictive control with the particle swarm optimization by embedding the two-step prediction into the particle updating process, and the new particles are tested to avoid formation of infeasible particles. Moreover, the environment is modeled based on the grid method, and the coverage information and uncertainty of the environment are stored in the vector grid. Simulation is conducted to verify the improved algorithm in island monitoring scenario. The results show that the proposed algorithm achieves multi-AUV patrol path planning for complex environment, and different patrol frequency for the regions with different nature to increase the patrol efficiency
- multi-AUV /
- cooperative path planning /
- predictive control /
- particle swarm optimization

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参考文献(10)

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