Coverage path planning algorithm of unmanned surface vehicle based on ocean remote sensing images

被引：0
|
作者：
Cao Y. [1 ,2 ]
Cheng X. [1 ,2 ]
Li D. [1 ,2 ]
Liu F. [1 ,2 ]
机构：
[1] Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, Southeast University, Nanjing
[2] School of Instrument Science & Engineering, Southeast University, Nanjing
来源：
Zhongguo Guanxing Jishu Xuebao/Journal of Chinese Inertial Technology | 2023年 / 31卷 / 01期
关键词：
greedy algorithm; improved YOLO V3; path coverage; remote sensing image; rotating beams; unmanned surface vehicle;
D O I：
10.13695/j.cnki.12-1222/o3.2023.01.013
中图分类号：
U674 [各种船舶];
学科分类号：
摘要：
The combination of remote sensing technology and unmanned surface vehicle has great potential in ocean coverage applications. A coverage path planning (CPP) algorithm for unmanned surface vehicle based on ocean remote sensing images is proposed. Firstly, to establish an accurate map model, a rotating target detection algorithm based on improved YOLO V3 is proposed. Based on YOLO V3, the axis, length, width, and coordinate information of obstacles are refined to improve the recall rate of target detection in complex scenes without increasing the amount of calculation. Then, to obtain effective coverage path, a CPP algorithm based on rotating beams and greedy algorithm is proposed. The algorithm divides the complete path into straight paths and turning paths, and optimizes the coverage path based on the length and obstacle avoidance objectives respectively. Simulation results show that, compared with the neuronal excitation algorithm based on grid map, the length of this algorithm is reduced by 9.3%, and the coverage rate is100% and the repetition rate is less than 2.1% in two extreme ocean environments. © 2023 Editorial Department of Journal of Chinese Inertial Technology. All rights reserved.
引用
收藏
页码：85 / 91
页数：6
相关论文
共 14 条
  • [1] Hu C, Wen L, Zhang Y, Et al., Cooperative navigation for USV/UUV based on ultra-short baseline positioning system, Journal of Chinese Inertial Technology, 27, pp. 327-333, (2019)
  • [2] Ai B, Jia M, Xu H, Et al., Coverage path planning for maritime search and rescue using reinforcement learning, Ocean Engineering, 241, (2021)
  • [3] Miao R, Pang S, Jiang D, Et al., Complete coverage path planning for autonomous marine vehicle used in multi-bay areas, Acta Geodaetica et Cartographica Sinica, 48, 2, (2019)
  • [4] Cheng X, Qi Y., Indoor indicator path planning algorithm based on grid method, Journal of Chinese Inertial Technology, 26, pp. 236-240, (2018)
  • [5] Zhu D, Tian C, Sun B, Et al., Complete coverage path planning of autonomous underwater vehicle based on GBNN algorithm, Journal of Intelligent and Robotic Systems, 94, 1, pp. 237-249, (2018)
  • [6] Lyu X, Cheng Q, Li S, Et al., Unmanned surface vehicle full traversal path planning based on improved A* algorithm, Journal of Unmanned Undersea Systems, 27, pp. 695-703, (2019)
  • [7] Ma Y, Zhao Y, Li Z, Et al., A new coverage path planning algorithm for unmanned surface mapping vehicle based on A-star based searching, Applied Ocean Research, 123, (2022)
  • [8] Wang Z, Jing P, Zhu B., Coverage path planning of mobile robot based on neuronal excitation, Control Engineering of China, 24, pp. 283-286, (2017)
  • [9] Wang Y, Wang W, Xu F, Et al., Path planning approach based on improved ant colony optimization for sprayer UAV, Transactions of The Chinese Society of Agricultural Machinery, 51, 11, pp. 103-112, (2020)
  • [10] Shivgan R, Dong Z., Energy-efficient drone coverage path planning using genetic algorithm, 2020 IEEE 21st International Conference on High Performance Switching and Routing (HPSR), pp. 1-6, (2020)
12