基于改进混合运动麻雀搜索算法的水电站ROV路径规划

曾学文; 黄秀华; 陈敏; 周达; 张福林

doi:10.11993/j.issn.2096-3920.2023-0162

基于改进混合运动麻雀搜索算法的水电站ROV路径规划

doi: 10.11993/j.issn.2096-3920.2023-0162

1.
国家能源集团江西电力有限公司, 江西南昌, 330000
2.
国家能源集团江西电力有限公司万安水力发电厂, 江西吉安, 343800
3.
中国电力建设集团(广东) 工程监测检测技术有限公司, 广东广州, 510000

详细信息

作者简介:
曾学文(1984-), 男, 工程师, 主要研究方向为清洁智能发电及能源动力控制

中图分类号: TJ630.34; U666
计量
- 文章访问数: 136
- HTML全文浏览量: 68
- PDF下载量: 31
- 被引次数: 0
出版历程
- 收稿日期: 2023-12-12
- 修回日期: 2024-01-03
- 录用日期: 2024-02-07
- 网络出版日期: 2024-03-12

ROV Path Planning of Hydropower Plants Based on Improved Hybrid Motion Sparrow Search Algorithm

1.
Jiangxi Electric Power Company Limited, National Energy Group, Nanchang 330000, China
2.
Wan’an Hydropower Power Plant, National Energy Group Jiangxi Electric Power Company Limited, Ji’an 343800, China
3.
China Electric Power Construction Group(Guangdong) Engineering Monitoring and Testing Technology Co, Guangzhou 510000, China

摘要

摘要: 水下遥控机器人(ROV)路径规划是水电站水下巡检作业的关键。针对电站水库下复杂环境及现有路径规划算法存在规划时间长、算法稳定性差、易陷入局部最优及生成路径不平滑等问题, 提出一种基于改进混合运动麻雀搜索算法的水电站ROV路径规划方法。首先, 引入佳点集改进麻雀种群初始化方法, 提高了种群多样性; 其次, 提出混合运动策略优化麻雀群体位置更新方式, 提高了算法收敛精度及稳定性; 然后, 结合工程实际问题, 考虑水库下水流速度大、强磁场、障碍物以及投入成本等因素，建立了包含时间成本、路径威胁、水流扰动和偏置函数的多元目标函数; 最后, 采用三次B样条插值得到最优光滑路径。仿真结果表明, 相较于其他路径规划算法, 所提方法在计算精度、收敛速度和稳定性方面表现更好, 适用于水电站水下巡检任务。
- 水下遥控机器人 /
- 路径规划 /
- 麻雀搜索算法
Abstract: Path planning for underwater remotely operated vehicles(ROVs) is a prerequisite for underwater inspection operation of hydropower plants. The reservoir of hydropower plants has complex environments, and the existing path planning algorithms face the problems of long planning time, poor stability of algorithms, easy fall into the local optimum, and unsmooth path generation. In view of these issues, this paper put forward a ROV path planning method for hydropower plants based on the improved hybrid motion sparrow search algorithm. Firstly, the good point set was introduced to improve the sparrow population initialization method, which enhanced the population diversity; secondly, the hybrid motion strategy was proposed to optimize the sparrow population position updating method, increasing the algorithm’s convergence accuracy and stability; then, the multivariate objective function, which contained time cost, path threat, current disturbance, and penalty function, was established by combining with the actual engineering problems and considering the factors of large flow velocity of reservoirs, strong magnetic field, obstacles, and cost; finally, the triple B-spline interpolation was used to obtain the optimal smooth path. The simulation results show that compared with other path planning algorithms, the proposed method performs better in terms of computational accuracy, convergence speed, and stability, and it is suitable for underwater inspection tasks of hydropower plants.
- remotely operated vehicle /
- path planning /
- sparrow search algorithm

HTML全文

图 1 种群分布对比

Figure 1. Comparison of population distribution

下载: 全尺寸图片幻灯片

图 2 算法流程

Figure 2. Flow chart of the algorithm

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图 3 仿真环境建模图

Figure 3. Diagram of simulation environment modeling

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图 4 不同仿真算法下ROV路径规划结果对比

Figure 4. Results of ROV path planing under different simulation algorithms

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图 5 不同算法耗时对比

Figure 5. Time consumption comparison of different algorithms

下载: 全尺寸图片幻灯片

表 1 不同算法实验性能对比

Table 1. Experimental performance comparsion of different algorithms

算法	T_best/s	T_mean/s	S_TD/s	P_s/%	T_single/s
PSO	87.09	87.91	36.91	54.95	11.25
BOA	86.59	88.45	28.27	62.42	9.87
WOA	85.24	86.74	32.69	78.36	10.32
SSA	84.12	85.37	23.45	79.41	8.57
SSA-C	82.04	83.25	19.27	85.96	8.24
IMMSSA	79.94	81.02	12.74	97.42	8.06

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