AUV swarm path planning based on elite family genetic algorithm

被引：0
|
作者：
Feng H. [1 ,2 ]
Hu Q. [1 ,2 ]
Zhao Z. [1 ,2 ]
机构：
[1] School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an
[2] Shaanxi Key Laboratory of Intelligent Robots, Xi'an
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2022年 / 44卷 / 07期
关键词：
Autonomous underwater vehicle (AUV) swarm; Elite family strategy; Genetic algorithm (GA); Multi-agent path planning (MAPP); Multi-path planning;
D O I：
10.12305/j.issn.1001-506X.2022.07.21
中图分类号：
学科分类号：
摘要：
Aiming at the defect that the traditional path planning algorithm can only plan a single shortest path and can not adjust the path width, which is difficult to apply to the cluster route planning of autonomous underwater vehicle (AUV), a genetic algorithm based on elite family (EFGA) is proposed. In this algorithm, gene fitness is added to the fitness evaluation function, and elite individuals are marked as the result of multi-path planning in the process of evolution. Based on this algorithm, a multi-agent path planning (MAPP) method is designed for AUV cluster path planning. Simulation results show that the algorithm can solve the conflict free path set, realize MAPP, reduce the navigation time of underwater vehicle cluster by realizing the optimal multi-path navigation scheme of AUV cluster, and meet the requirements of adjustable path width for different AUV formation sizes. © 2022, Editorial Office of Systems Engineering and Electronics. All right reserved.
引用
收藏
页码：2251 / 2262
页数：11
相关论文
共 30 条
  • [1] WEI T, MIAO Y, XIAO B Q, Et al., Research on autonomous control technology of underwater unmanned swarm based on center transfer, Proc.of the 5th International Conference on Automation, Control and Robotics Engineering, pp. 430-434, (2020)
  • [2] TAO W, ZHANG X S., Research on application and key technology of underwater unmanned cluster abroad, Ship Electronic Engineering, 41, 2, pp. 9-13, (2021)
  • [3] MAGNUSON S., DARPA pushes "Mosaic Warfare" concept, National Defense, 103, 780, pp. 18-19, (2018)
  • [4] HUANG Y, LI Y, YU J C, Et al., State-of-the-art and development trends of AUV intelligence, Jiqiren/Robot, 42, 2, pp. 215-231, (2020)
  • [5] CAO X, SUN H B, JAN G E., Multi-AUV cooperative target search and tracking in unknown underwater environment, Ocean Engineering, 150, pp. 1-11, (2018)
  • [6] ZHU D Q, ZHOU B, YANG S X., A novel algorithm of multi-AUVs task assignment and path planning based on biologically inspired neural network map, IEEE Trans.on Intelligent Vehicles, 6, 2, pp. 333-342, (2021)
  • [7] YAN Z P, ZHANG J Z, ZENG J, Et al., Water wave optimization algorithm for autonomous underwater vehicle path planning problem, Journal of Intelligent & Fuzzy Systems, 40, 5, pp. 9127-9141, (2021)
  • [8] LIU Q Z, WU F., Research progress of multi-agent path planning, Computer Engineering, 46, 4, pp. 1-10, (2020)
  • [9] BABEL L., Coordinated target assignment and UAV path planning with timing constraints, Journal of Intelligent & Robotic Systems, 94, 3, pp. 857-869, (2019)
  • [10] LIU M Y, XU B G, PENG X G., Cooperative path planning for multi-AUV in time-varying ocean flows, Journal of Systems Engineering and Electronics, 27, 3, pp. 612-618, (2016)
123